ArticleBioengineering (Basel, Switzerland)2022
A Deep Learning Computer-Aided Diagnosis Approach for Breast Cancer.
Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Magnification-independent breast cancer diagnosis using a GWO-enhanced vision transformer with multi-stage stain normalization.Scientific reports · 2026Article
- HMC-net: a ResNet fused hierarchical multi-scale cross-attention architecture for mammographic breast malignancy recognition incorporating explainable AI.Frontiers in oncology · 2026Article
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- CTCNet: a fine-grained classification network for fluorescence images of circulating tumor cells.Medical & biological engineering & computing · 2025Article
- Equilibrium Optimization-Based Ensemble CNN Framework for Breast Cancer Multiclass Classification Using Histopathological Image.Diagnostics (Basel, Switzerland) · 2024Article
- Integrated analysis of diverse cancer types reveals a breast cancer-specific serum miRNA biomarker through relative expression orderings analysis.Breast cancer research and treatment · 2024Article
- Multi-Instance Classification of Breast Tumor Ultrasound Images Using Convolutional Neural Networks and Transfer Learning.Bioengineering (Basel, Switzerland) · 2023Article
- Detection of Corneal Ulcer Using a Genetic Algorithm-Based Image Selection and Residual Neural Network.Bioengineering (Basel, Switzerland) · 2023Article
- Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted.Diagnostics (Basel, Switzerland) · 2023Article
- An improved multi-scale gradient generative adversarial network for enhancing classification of colorectal cancer histological images.Frontiers in oncology · 2023Article
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Authors and funding
5 authors.
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Abstract
Breast cancer is a gigantic burden on humanity, causing the loss of enormous numbers of lives and amounts of money. It is the world's leading type of cancer among women and a leading cause of mortality and morbidity. The histopathological examination of breast tissue biopsies is the gold standard for diagnosis. In this paper, a computer-aided diagnosis (CAD) system based on deep learning is developed to ease the pathologist's mission. For this target, five pre-trained convolutional neural network (CNN) models are analyzed and tested-Xception, DenseNet201, InceptionResNetV2, VGG19, and ResNet152-with the help of data augmentation techniques, and a new approach is introduced for transfer learning. These models are trained and tested with histopathological images obtained from the BreakHis dataset. Multiple experiments are performed to analyze the performance of these models through carrying out magnification-dependent and magnification-independent binary and eight-class classifications. The Xception model has shown promising performance through achieving the highest classification accuracies for all the experiments. It has achieved a range of classification accuracies from 93.32% to 98.99% for magnification-independent experiments and from 90.22% to 100% for magnification-dependent experiments.
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