CerCan.Net: Cervical cancer classification model via multi-layer feature ensembles of lightweight CNNs and transfer learning

Article Chemistry, Multidisciplinary

Cervical Cancer Diagnosis Based on Multi-Domain Features Using Deep Learning Enhanced by Handcrafted Descriptors

Omneya Attallah

Summary: Routine checks can help prevent cervical cancer. Artificial intelligence can enhance the traditional testing procedure and improve diagnostic accuracy.

APPLIED SCIENCES-BASEL (2023)

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Article Medicine, General & Internal

GabROP: Gabor Wavelets-Based CAD for Retinopathy of Prematurity Diagnosis via Convolutional Neural Networks

Omneya Attallah

Summary: Retinopathy of prematurity (ROP) is a serious ocular problem in premature infants. This paper proposes an automated CAD tool called GabROP, which uses Gabor wavelets and multiple deep learning models for ROP diagnosis. GabROP analyzes fundus images using Gabor wavelets and trains three CNN models independently. The features from these models are fused using discrete cosine transform (DCT) to obtain the final diagnosis result. Experimental results demonstrate the accuracy and efficiency of GabROP.

DIAGNOSTICS (2023)

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Article Computer Science, Software Engineering

Removal of noise on mammogram breast images using filtering methods

Swapna Mudrakola et al.

Summary: Detecting breast cancer at an early stage is crucial for reducing mortality rates. Mammography, as a preliminary examination, utilizes image processing and filtering methods to enhance the accuracy of tumor detection.

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE (2023)

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Article Horticulture

Tomato Leaf Disease Classification via Compact Convolutional Neural Networks with Transfer Learning and Feature Selection

Omneya Attallah

Summary: Tomatoes are valuable vegetables and an economic pillar for many countries. Automatic identification of tomato leaf diseases using deep learning models has been widely studied, but existing methods suffer from high computational complexity and large dimensionality. This study proposes a pipeline that utilizes compact convolutional neural networks and transfer learning for condensed and high-level representation. The pipeline also applies a hybrid feature selection approach to reduce dimensions and achieves high accuracy in tomato leaf disease identification.

HORTICULTURAE (2023)

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Article Biology

Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: From convolutional neural networks to visual transformers

Wanli Liu et al.

Summary: Cervical cancer is a common and fatal cancer in women, and a computer-aided diagnosis system based on deep learning has been developed to assist in screening. Resizing clinical medical images directly affects accuracy due to inconsistent dimensions. However, existing studies have shown that deep learning models are robust to changes in image aspect ratios.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

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Article Engineering, Biomedical

A Hybrid Deep Learning Model for Predicting Molecular Subtypes of Human Breast Cancer Using Multimodal Data

T. Liu et al.

Summary: In this study, a Hybrid DL model based on multimodal data is proposed for the prediction of breast cancer subtypes. By combining patient's gene modality data with image modality data and fusing features using weighted linear aggregation, a more accurate and efficient prediction of breast cancer subtypes is achieved.

IRBM (2022)

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Article Computer Science, Information Systems

Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis

Anupam Das

Summary: COVID-19 is a major health calamity in the 21st century, with developing countries facing delays in recognizing cases. The combination of medical images and deep learning classifiers offer more hopeful results with high accuracy in predicting and recognizing COVID-19 cases.

MULTIMEDIA TOOLS AND APPLICATIONS (2022)

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Article Biology

AI-Based Pipeline for Classifying Pediatric Medulloblastoma Using Histopathological and Textural Images

Omneya Attallah et al.

Summary: Pediatric medulloblastomas (MBs), the most common malignant brain tumors in children, are heterogenous and challenging to classify accurately based on histopathological images. This study combines textural analysis and deep learning techniques to improve the subtype identification of pediatric MBs. The automated pipeline proposed in this study shows an increased accuracy in classification compared to previous methods, providing a powerful tool for individualized therapies and identification of high-risk complications in children.

LIFE-BASEL (2022)

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Article Computer Science, Software Engineering

Pap smear based cervical cancer detection using residual neural networks deep learning architecture

Vijayanand Sellamuthu Palanisamy et al.

Summary: A modified deep learning algorithm based on DTCWT is proposed for classifying Pap smear cell images with an automatic detection index of about 99%.

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE (2022)

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Article Engineering, Biomedical

Exemplar pyramid deep feature extraction based cervical cancer image classification model using pap-smear images

Orhan Yaman et al.

Summary: Cervical cancer is a common type of cancer in women worldwide, and early detection is crucial for treatment. This study proposes a method based on exemplar pyramid deep feature extraction for the diagnosis of cervical cancer, achieving high accuracy through machine learning and computer vision techniques in classifying and diagnosing cervical cells in pap-smear images.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

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Article Computer Science, Interdisciplinary Applications

A fuzzy distance-based ensemble of deep models for cervical cancer detection

Rishav Pramanik et al.

Summary: This article proposes a fuzzy distance-based ensemble approach composed of deep learning models for cervical cancer detection in PaP smear images. By applying three transfer learning models and ensemble techniques, higher performance is achieved compared to individual models.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2022)

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Article Computer Science, Artificial Intelligence

Evaluation of a new dataset for visual detection of cervical precancerous lesions

Ying Zhang et al.

Summary: Automated visual evaluation (AVE) is a promising method for detecting and diagnosing cervical precancerous lesions through deep learning classifier analysis of images. The introduction of a new dataset (EVA dataset) collected using a mobile colposcope shows potential challenges for high-grade SIL diagnosis and AVE classifier development. The results suggest that a deep learning framework is effective for high-grade SIL diagnosis but improvements are needed, especially for the EVA dataset.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

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Article Chemistry, Analytical

An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques

Omneya Attallah

Summary: Accurate and rapid diagnosis of COVID-19 is crucial for controlling its spread, reducing lockdown restrictions, and alleviating the burden on healthcare systems. This study introduces a novel automated diagnostic tool based on ECG data, utilizing deep learning models to achieve high accuracy in diagnosing COVID-19 at binary and multiclass levels.

BIOSENSORS-BASEL (2022)

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Article Biology

A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models

Nahid Ferdous Aurna et al.

Summary: This study proposes a novel approach using deep Convolutional Neural Networks (CNN) for precise and automatic classification of brain tumors through two-stage feature ensemble. The robustness of the proposed model is demonstrated through performance metrics and experiments, showing its superiority over existing models.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

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Article Computer Science, Artificial Intelligence

Filter group delays equalization for 2D discrete wavelet transform applications

Mohammed Bahoura et al.

Summary: This paper presents a new approach for near-perfect image reconstruction in online/real-time image analysis/synthesis applications. By equalizing filter delays, the peak signal-to-noise ratios of the reconstructed images have been significantly improved.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

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Article Computer Science, Artificial Intelligence

A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects

Zewen Li et al.

Summary: This review provides insights into the development history of CNN, a overview of various convolutions, introduction to classic and advanced CNN models, conclusions drawn from experimental analysis, rules of thumb for function and hyperparameter selection, and applications of 1-D, 2-D, and multidimensional convolutions. Moreover, it also discusses open issues and promising directions for CNN as guidelines for future work.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

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Article Engineering, Electrical & Electronic

Early cervical cancer diagnosis using Sooty tern-optimized CNN-LSTM classifier

B. Chitra et al.

Summary: This study proposes a novel approach for cervical cancer classification, utilizing image pre-processing, segmentation, feature extraction, and the combination of convolutional neural network and Sooty Tern Optimization algorithm. The proposed method achieves accurate classification of cervical cancer and demonstrates better results compared to other classifiers, with high accuracy, specificity, sensitivity, and F-score.

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY (2022)

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Article Biotechnology & Applied Microbiology

Cervical Net: A Novel Cervical Cancer Classification Using Feature Fusion

Hiam Alquran et al.

Summary: This study proposes a computer-aided diagnosis method for cervical cancer using deep learning and feature fusion. The proposed method includes image processing, feature extraction and selection, as well as classification using machine learning algorithms. Experimental results show that the proposed method achieves high accuracy in multiple categories.

BIOENGINEERING-BASEL (2022)

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Article Computer Science, Artificial Intelligence

Applications of dynamic feature selection and clustering methods to medical diagnosis

Mohammad Mahdi Ershadi et al.

Summary: Machine learning methods are commonly used for disease and cancer diagnosis. This study proposes a dynamic feature selection method based on principal component analysis and Wrapper feature selection methods, combined with three clustering methods and two deep learning classifiers. The proposed methods show significant improvements compared to existing methods.

APPLIED SOFT COMPUTING (2022)

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Article Computer Science, Hardware & Architecture

Modified metaheuristics with stacked sparse denoising autoencoder model for cervical cancer classification

Thavavel Vaiyapuri et al.

Summary: In this study, a novel cervical cancer classification model based on deep learning and modified firefly optimization algorithm is proposed for automated classification of Pap smear images. Through steps such as denoising, image segmentation, and feature extraction, the model can accurately determine the presence of cervical cancer cells in the Pap smear images, and experimental validation highlighted its superior performance compared to other recent approaches.

COMPUTERS & ELECTRICAL ENGINEERING (2022)

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Article

PSO + GWO: a hybrid particle swarm optimization and Grey Wolf optimization based Algorithm for fine-tuning hyper-parameters of convolutional neural networks for Cardiovascular Disease Detection

Serhat KILIÇARSLAN

Journal of Ambient Intelligence and Humanized Computing (2022)

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Proceedings Paper Computer Science, Artificial Intelligence

What Makes Transfer Learning Work for Medical Images: Feature Reuse & Other Factors

Christos Matsoukas et al.

Summary: Transfer learning is a technique that transfers knowledge from one domain to another. In the field of medical imaging, transferring features from ImageNet is commonly used, but it is unclear how effective and what factors determine the success of transfer learning in the medical domain. Through experiments on medical image benchmark datasets, this study explores the impact of data size, model capacity and inductive bias, as well as the distance between the source and target domains on transfer learning. The findings suggest that transfer learning is beneficial in most cases, and feature reuse plays a crucial role in its success.

2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2022)

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Article Health Care Sciences & Services

A deep learning-based diagnostic tool for identifying various diseases via facial images

Omneya Attallah

Summary: With the current health crisis caused by the COVID-19 pandemic, patients' preference for limited contact with doctors or clinicians has led to the development of computer-aided facial diagnosis systems. This study introduces FaceDisNet, a novel system that utilizes deep learning techniques and a new public dataset to diagnose single and multiple diseases accurately without physical contact with patients. The high accuracy achieved by FaceDisNet demonstrates its reliability and potential for assisting physicians in manual diagnosis.

DIGITAL HEALTH (2022)

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Article Health Care Sciences & Services

A computer-aided diagnostic framework for coronavirus diagnosis using texture-based radiomics images

Omneya Attallah

Summary: The accurate and rapid detection of novel coronavirus is crucial to prevent its spread, and artificial intelligence techniques can aid in this process. This study proposes a computer-assisted diagnostic framework based on deep learning and texture-based radiomics approaches. By fusing deep features from multiple convolutional neural networks, the diagnostic accuracy is improved. The performance of this framework allows radiologists to achieve fast and accurate diagnosis of coronavirus.

DIGITAL HEALTH (2022)

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Article Computer Science, Artificial Intelligence

Quantitative detection of cervical cancer based on time series information from smear images

C. W. Zhang et al.

Summary: A cervical quantitative detection framework combining fine-tuned Long Short-Term Memory Fully Convolutional Network and Fuzzy Nonlinear Regression is proposed to improve the detection performance of cervical cancer, achieving high accuracy and specificity through time series-based screening.

APPLIED SOFT COMPUTING (2021)

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Article Computer Science, Artificial Intelligence

An ensemble method for nuclei detection of overlapping cervical cells

Debora Nasser Diniz et al.

Summary: The Pap test is a preventive approach for tracking potentially cancerous cells, requiring labor-intensive cytological preparations. The proposed ensemble method for cervical nuclei detection aims to reduce the workload of cytopathologists. Experiments show that this method is the state-of-the-art algorithm in terms of recall and F1 values.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

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Article Medicine, General & Internal

MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI

Omneya Attallah

Summary: Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor. Precise diagnosis is essential. The computer-aided diagnosis system MB-AI-His combines deep learning and texture analysis for automatic diagnosis of pediatric MB and its subtypes from histopathological images efficiently and reliably.

DIAGNOSTICS (2021)

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Article Computer Science, Artificial Intelligence

Cervical cancer detection in pap smear whole slide images using convNet with transfer learning and progressive resizing

Anant R. Bhatt et al.

Summary: The study introduces a novel methodology for multiclass classification of cervical cells, with focus on optimized feature extraction for disease diagnosis. Through the utilization of Progressive Resizing technique and Transfer Learning technique, a substantial performance increase has been achieved in training Conv Net models, resulting in benchmark scores on the Herlev Dataset.

PEERJ COMPUTER SCIENCE (2021)

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Article Computer Science, Artificial Intelligence

GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases

Omneya Attallah et al.

Summary: The article proposes a CADx system named Gastro-CADx for classifying multiple GI diseases using DL techniques. The system consists of three stages: extracting spatial features using CNN, extracting temporal-frequency and spatial-frequency features using DWT and DCT, and fusing multiple feature combinations for optimal output selection.

PEERJ COMPUTER SCIENCE (2021)

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Article Computer Science, Hardware & Architecture

CytoBrain: Cervical Cancer Screening System Based on Deep Learning Technology

Hua Chen et al.

Summary: The study developed an AI system named CytoBrain for automatic screening of abnormal cervical cells to aid in clinical diagnosis. The system consists of three main modules: cervical cell segmentation, cell classification based on CompactVGG, and human-aided diagnosis. CompactVGG showed superior performance in terms of time efficiency and classification accuracy, supporting large-scale cervical cancer screening.

JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY (2021)

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Article Medicine, General & Internal

DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity

Omneya Attallah

Summary: Retinopathy of Prematurity (ROP) affects preterm neonates and could lead to blindness. Deep Learning (DL) can assist ophthalmologists in diagnosing ROP, and the automated diagnostic tool DIAROP achieved an accuracy of 93.2%.

DIAGNOSTICS (2021)

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Article Computer Science, Artificial Intelligence

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

Omneya Attallah et al.

Summary: Breast cancer is a common cancer affecting females globally, and late diagnosis can lead to severe consequences. The Histo-CADx system proposed in this study successfully improves the accuracy of breast cancer diagnosis through two fusion stages.

PEERJ COMPUTER SCIENCE (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories

Omneya Attallah et al.

Summary: Skin cancer rates are increasing annually and early diagnosis is crucial. Artificial intelligence techniques have shown success in developing automated dermatologist tools for accurate classification, potentially improving diagnosis accuracy.

CONTRAST MEDIA & MOLECULAR IMAGING (2021)

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Article Multidisciplinary Sciences

A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

Ankur Manna et al.

Summary: This research proposes an ensemble method for cervical cancer single cell and whole slide image classification by combining three different convolutional neural network architectures. The proposed model is evaluated on two publicly available datasets, showing high classification accuracy and sensitivity.

SCIENTIFIC REPORTS (2021)

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Article Mathematical & Computational Biology

CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes

Omneya Attallah

Summary: Childhood medulloblastoma (MB) is a common malignant tumor, and early and accurate classification is crucial. CoMB-Deep combines deep learning and texture analysis feature extraction techniques to effectively classify MB and improve diagnostic accuracy.

FRONTIERS IN NEUROINFORMATICS (2021)

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Article Imaging Science & Photographic Technology

Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

Emanuela Paladini et al.

Summary: Automatic tissue phenotyping has gained increasing interest in the field of Digital Pathology, specifically for diagnosing Colorectal Cancer and distinguishing between different cancer grades. The study shows that deep learning methods outperform hand-crafted feature-based methods and CNN architectures in CRC tissue type classification.

JOURNAL OF IMAGING (2021)

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Review Computer Science, Theory & Methods

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Laith Alzubaidi et al.

Summary: Deep learning has become the gold standard in the machine learning community, widely used in various domains and capable of learning massive data. Through a comprehensive survey, a better understanding of the most important aspects of deep learning is provided.

JOURNAL OF BIG DATA (2021)

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Article Computer Science, Artificial Intelligence

A fully-automated deep learning pipeline for cervical cancer classification

Zaid Alyafeai et al.

EXPERT SYSTEMS WITH APPLICATIONS (2020)

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Residual learning based CNN for breast cancer histopathological image classification

Mahesh Gour et al.

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY (2020)

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Selective feature connection mechanism: Concatenating multi-layer CNN features with a feature selector

Chen Du et al.

PATTERN RECOGNITION LETTERS (2020)

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Deep Learning Techniques for Automatic Detection of Embryonic Neurodevelopmental Disorders

Omneya Attallah et al.

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Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis

Marc Arbyn et al.

LANCET GLOBAL HEALTH (2020)

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An Effective Mental Stress State Detection and Evaluation System Using Minimum Number of Frontal Brain Electrodes

Omneya Attallah

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Discrete wavelet transform based data representation in deep neural network for gait abnormality detection

Jayeeta Chakraborty et al.

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Deep Learning for Feature Extraction in Remote Sensing: A Case-Study of Aerial Scene Classification

Biserka Petrovska et al.

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A comprehensive study on the multi-class cervical cancer diagnostic prediction on pap smear images using a fusion-based decision from ensemble deep convolutional neural network

Elima Hussain et al.

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Liquid based-cytology Pap smear dataset for automated multi-class diagnosis of pre-cancerous and cervical cancer lesions

Elima Hussain et al.

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Correction Oncology

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CA-A CANCER JOURNAL FOR CLINICIANS (2020)

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Cervical image classification based on image segmentation preprocessing and a CapsNet network model

XiaoQing Zhang et al.

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Deep Learning in the Biomedical Applications: Recent and Future Status

Ryad Zemouri et al.

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Cervical Cancer, Different Treatments and Importance of Bile Acids as Therapeutic Agents in This Disease

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Breast cancer histopathology image classification through assembling multiple compact CNNs

Chuang Zhu et al.

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Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods

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Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion

Syed Umar Amin et al.

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A survey on Image Data Augmentation for Deep Learning

Connor Shorten et al.

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Automatic Hyperparameter Optimization for Transfer Learning on Medical Image Datasets Using Bayesian Optimization

Rune Johan Borgli et al.

2019 13TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION AND COMMUNICATION TECHNOLOGY (ISMICT) (2019)

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Multi-Layer Convolutional Features Concatenation With Semantic Feature Selector for Vein Recognition

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Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction

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Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging

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Integrating Multilayer Features of Convolutional Neural Networks for Remote Sensing Scene Classification

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Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks

Reza Rasti et al.

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Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention

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Minimum redundancy maximum relevance feature selection approach for temporal gene expression data

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CerCan.Net: Cervical cancer classification model via multi-layer feature ensembles of lightweight CNNs and transfer learning

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