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This is a Quantitative Study study.

September 30, 2025

Enhanced Comparative Study of Deep Learning and Hybrid Models for Breast Cancer Histopathology Image Classification

Key Points

The CNN model achieved a remarkable accuracy of 96.81% in detecting malignant breast cancer from mammograms.
Using the extensive CBIS-DDSM dataset, the study emphasizes the high performance of deep learning in histopathology image classification.
The study involved analyzing 277,524 image patches, labeled for cancerous content, highlighting the dataset's depth.
Results indicate that CNN-based models can significantly enhance computer-aided diagnosis systems for breast cancer detection.

Abstract

Using mammography images from the CBIS-DDSM (Curated Breast Imaging Subset of the Digital Database for Screening Mammography), an extensive dataset of digitized mammograms, deep learning techniques are used in this work to classify breast cancer. A total of 277,524 50 x 50-pixel image patches were extracted and labeled according to whether they were malignant. This dataset was used to train a custom Convolutional Neural Network (CNN) that can classify regions as either cancerous or non-cancerous. With a 96.81% accuracy rate on the test set, the model showed excellent performance and generalizability. With potential uses in computer-aided diagnosis systems, this study highlights the efficacy of CNN-based models in automated breast cancer detection from mammography.

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Enhanced Comparative Study of Deep Learning and Hybrid Models for Breast Cancer Histopathology Image Classification

Key Points

Abstract

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