Chaotic Sparrow Search Algorithm with Deep Transfer Learning Enabled Breast Cancer Classification on Histopathological Images

Article Computer Science, Software Engineering

Transfer learning-assisted multi-resolution breast cancer histopathological images classification

Nouman Ahmad et al.

Summary: Breast cancer is a leading cause of death among women, and machine learning and deep learning techniques are widely used in its diagnosis, improving decision consistency and reducing errors in the healthcare field.

VISUAL COMPUTER (2022)

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Article Biotechnology & Applied Microbiology

Detection of Breast Cancer Using Histopathological Image Classification Dataset with Deep Learning Techniques

V. K. Reshma et al.

Summary: Cancer, especially breast cancer, is a major cause of mortality worldwide. Various imaging modalities and histopathology studies can be used to diagnose breast cancer. This study focuses on developing improved strategies for computer-aided diagnosis (CAD) to reduce observer variability. It proposes an automatic segmentation method and incorporates spatial information to enhance segmented images, achieving fast and accurate analysis. Additionally, a classification strategy based on weighted feature selection and a Convolutional Neural Network Classifier is developed, aiming to improve the accuracy of breast cancer detection.

BIOMED RESEARCH INTERNATIONAL (2022)

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Review Computer Science, Interdisciplinary Applications

Breast Cancer Detection, Segmentation and Classification on Histopathology Images Analysis: A Systematic Review

R. Krithiga et al.

Summary: Digital pathology represents a major evolution in modern medicine, especially in predicting breast cancer recurrence rates. However, challenges such as data analysis difficulties need to be addressed for further research and development advancements.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2021)

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Article Engineering, Biomedical

Automatic classification of breast cancer histopathological images based on deep feature fusion and enhanced routing

Pin Wang et al.

Summary: The paper proposes a method for breast cancer histopathological image classification based on deep feature fusion and enhanced routing. The method was tested on the BreaKHis dataset, showing efficient performance for breast cancer classification in clinical settings.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

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Article Computer Science, Artificial Intelligence

Going deeper: magnification-invariant approach for breast cancer classification using histopathological images

S. Alkassar et al.

Summary: Breast cancer has the highest fatality rate among women compared to other cancers, emphasizing the importance of early diagnosis. A novel method has been proposed for diagnosing breast cancer based on magnification-specific classification, achieving promising results in terms of diagnostic accuracy.

IET COMPUTER VISION (2021)

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Article Engineering, Electrical & Electronic

Optimizing GIS partial discharge pattern recognition in the ubiquitous power internet of things context: A MixNet deep learning model

Yanxin Wang et al.

Summary: Utilizing deep learning models for partial discharge pattern recognition in the power Internet of Things can improve fault diagnosis accuracy while significantly reducing computational and storage costs. The proposed method has shown a recognition accuracy of 99.1% after validation, demonstrating its advantages in feature extraction and visualization of model performance.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2021)

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Article Multidisciplinary Sciences

A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images

Anabia Sohail et al.

Summary: The research proposes a deep learning based multi-phase mitosis detection framework for identifying mitotic nuclei in breast cancer tissue, achieving good discrimination ability and generalization on challenging datasets.

SCIENTIFIC REPORTS (2021)

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Article Engineering, Biomedical

DeepBreastNet: A novel and robust approach for automated breast cancer detection from histopathological images

Fatih Demir

Summary: The analysis of histopathological images is crucial for detecting the most insidious type of cancer for women, breast cancer. Artificial intelligence-based applications, particularly deep learning models, are effective tools for automated breast cancer detection due to their high performance in medical image classification. In this study, a novel approach using CLSTM model, MWSA pre-processing technique, and optimized SVM classifier showed significant performance improvements in binary and eight-class classification tasks for detecting breast cancer from histopathological images.

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING (2021)

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Article Computer Science, Information Systems

Sentiment Analysis Using Stacked Gated Recurrent Unit for Arabic Tweets

Asma Al Wazrah et al.

Summary: Over the past decade, the amount of Arabic content on websites and social media has increased significantly, allowing for rich sources for trend analysis through natural language processing tasks like sentiment analysis. Deep learning techniques, such as GRU and SBi-GRU, have been utilized to improve accuracy in analyzing unstructured data. Research has proposed neural models and ensemble methods for Arabic NLP, with the use of automatic sentiment refinement to discard stop words and achieve high accuracy in sentiment classification.

IEEE ACCESS (2021)

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Article Computer Science, Information Systems

Breast Cancer Classification From Histopathological Images Using Patch-Based Deep Learning Modeling

Irum Hirra et al.

Summary: Accurate detection and classification of breast cancer is crucial in medical imaging, and a novel deep learning method called Pa-DBN-BC was proposed in this study to achieve an accuracy of 86% on whole slide histopathology images by automatically extracting features through unsupervised pre-training and supervised fine-tuning phases.

IEEE ACCESS (2021)

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Article Computer Science, Information Systems

DMPPT Control of Photovoltaic Microgrid Based on Improved Sparrow Search Algorithm

Jianhua Yuan et al.

Summary: The study proposed a distributed maximum power point tracking method based on the sparrow search algorithm to solve the power mismatch loss issue in photovoltaic microgrid systems. By improving the algorithm with a center of gravity reverse learning mechanism and introducing a learning coefficient, it can track the maximum power point more accurately and quickly.

IEEE ACCESS (2021)

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Article Engineering, Electrical & Electronic

Breast cancer histopathology image classification using kernelized weighted extreme learning machine

Shweta Saxena et al.

Summary: Histopathology is the gold standard for diagnosing breast cancer, but imbalanced training datasets can skew machine learning models towards the majority class, potentially leading to misjudgments in diagnosis. This study introduces a hybrid ML model using pretrained ResNet50 and kernelized weighted extreme learning machine to address class imbalance in computer-aided diagnosis of breast cancer using histopathology. The proposed approach outperforms previous state-of-the-art ML models in handling both minority and majority class instances.

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY (2021)

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Article Biochemical Research Methods