Deep Transfer Learning-Based Breast Cancer Detection and Classification Model Using Photoacoustic Multimodal Images

Article Chemistry, Analytical

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

Kiran Jabeen et al.

Summary: This paper proposes a framework for breast cancer classification from ultrasound images using deep learning and feature fusion. The framework achieves high accuracy in early cancer detection, outperforming recent techniques.

SENSORS (2022)

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Article Biology

Deep learning prediction of axillary lymph node status using ultrasound images

Shawn Sun et al.

Summary: It is feasible to predict axillary lymph node metastasis from breast cancer ultrasound images using a convolutional neural network, which can potentially aid nodal staging in patients with breast cancer.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

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Article Biology

Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification on Ultrasound Images

Mahmoud Ragab et al.

Summary: This study developed an Ensemble Deep-Learning-Enabled Clinical Decision Support System for the diagnosis and classification of breast cancer using ultrasound images. The system utilizes image pre-processing, segmentation techniques, and deep learning models for feature extraction and machine learning classifier for breast cancer detection. The system aims to assist radiologists and healthcare professionals in the breast cancer classification process.

BIOLOGY-BASEL (2022)

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Article Computer Science, Information Systems

Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

Jose Escorcia-Gutierrez et al.

Summary: This study focuses on the diagnosis of breast cancer and proposes an automated deep learning model for digital mammogram-based breast cancer diagnosis. The model utilizes Gaussian filter-based pre-processing, Tsallis entropy-based image segmentation, and Deep Convolutional Neural Network-based Residual Network for feature extraction, with the wavelet neural network used for classification. The results show that the proposed model outperforms existing methods in terms of various performance measures.

CMC-COMPUTERS MATERIALS & CONTINUA (2022)

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Proceedings Paper Materials Science, Multidisciplinary

An integrated approach using CNN-RNN-LSTM for classification of fruit images

Harmandeep Singh Gill et al.

Summary: With the development of technology, computer and machine vision systems have been widely used in the agriculture sector. This article introduces a method that applies deep learning to fruit image classification, achieving higher accuracy by integrating CNN, RNN, and LSTM. Empirical results show that the proposed classification method is highly effective.

MATERIALS TODAY-PROCEEDINGS (2022)

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Article Computer Science, Artificial Intelligence

Adaptive morphological and bilateral filtering with ensemble convolutional neural network for pose-invariant face recognition

P. Naveen et al.

Summary: This paper explores face recognition using a biometric system, focusing on handling changes in pose, illumination, and expression. It introduces a new ECNN method and utilizes multi-task learning to improve recognition accuracy. The proposed approach effectively demonstrates improved performance on the entire multi-PIE dataset through experimentation.

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2021)

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Article Acoustics

A generic deep learning framework to classify thyroid and breast lesions in ultrasound images

Yi-Cheng Zhu et al.

Summary: The study developed an automatic scheme using deep convolutional neural networks to classify breast and thyroid lesions in ultrasound images, achieving higher classification accuracy than radiologists.

ULTRASONICS (2021)

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Review Acoustics

Review of deep learning for photoacoustic imaging

Changchun Yang et al.

Summary: Machine learning, especially deep learning, has shown significant advancement in medical image analysis, particularly in the field of photoacoustic imaging, with great potential for applications in medical imaging technology and healthcare issues.

PHOTOACOUSTICS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Multi-input deep learning approach for Cardiovascular Disease diagnosis using Myocardial Perfusion Imaging and clinical data

Ioannis D. Apostolopoulos et al.

Summary: This research explores the use of machine learning and deep learning methods to detect Coronary Artery Disease, finding that a hybrid multi-input network can match the accuracy of experts and achieve a classification accuracy of 79.15%, enhancing the robustness of the predictive model.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2021)

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Editorial Material Biochemical Research Methods

Translational Photoacoustic Imaging for Disease Diagnosis, Monitoring, and Surgical Guidance: introduction to the feature issue

Jun Xia et al.

Summary: This feature issue of Biomedical Optics Express covers all aspects of translational photoacoustic research, including applications in disease screening and diagnosis, imaging of disease progression and therapeutic response, and image-guided treatments. The issue also includes discussions on developments in photoacoustic instrumentation, contrast agents, image processing, and reconstruction algorithms.

BIOMEDICAL OPTICS EXPRESS (2021)

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Review Biology

Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review

Afshin Shoeibi et al.

Summary: Multiple Sclerosis (MS) is a brain disease that causes various issues and is diagnosed using MRI, which can be time-consuming and prone to errors. Utilizing a computer aided diagnosis system (CADS) based on artificial intelligence involves conventional machine learning and deep learning methods for MS diagnosis.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

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Article Mathematical & Computational Biology

Recognition of Thyroid Ultrasound Standard Plane Images Based on Residual Network

Minghui Guo et al.

Summary: This study proposes using a deep learning method based on a residual network to recognize the Thyroid Ultrasound Standard Plane. The ResNet-18 model achieved the best recognition effect on TUSP images with an average accuracy rate of 91.07%, showing potential to reduce misdiagnosis and missed diagnosis in clinical thyroid ultrasound examination.

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2021)

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Review Acoustics

Photoacoustic imaging for monitoring of stroke diseases: A review

Xi Yang et al.

Summary: Photoacoustic imaging method reconstructs object images with optical resolution and acoustic propagation, which is non-invasive and particularly attractive for stroke monitoring. Research shows significant progress in the application of photoacoustic imaging for stroke in both animal and human studies.

PHOTOACOUSTICS (2021)

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Review Health Care Sciences & Services

Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis

Ravi Aggarwal et al.

Summary: This study evaluated the diagnostic accuracy of deep learning algorithms in identifying pathology in medical imaging, finding good performance in ophthalmology, respiratory disease and breast imaging, but noting high heterogeneity between studies and extensive variation in methodology, outcome measures and terminology. The development of artificial intelligence-specific guidelines, particularly STARD, is recommended to address these issues.

NPJ DIGITAL MEDICINE (2021)

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Article Computer Science, Artificial Intelligence