Histopathological Image Analysis For Breast Cancer

Ameer Alam; Ishwar Chand; Ayush Jaiswal; Dr. Sanjay Kumar

doi:10.71097/IJSAT.v16.i2.4732

Histopathological Image Analysis For Breast Cancer

Author(s)	Ameer Alam, Ishwar Chand, Ayush Jaiswal, Dr. Sanjay Kumar
Country	India
Abstract	Breast cancer originates in breast cells and remains one of the most prevalent and life-threatening cancers among women, second only to lung cancer. This study introduces a Convolutional Neural Network (CNN)based method aimed at improving the automated detection of breast cancer through the analysis of histopathological images. The system leverages advanced CNN architectures to classify breast cancer images, emphasizing the identification of malignant tissue in whole slide images (WSIs). The dataset comprises 2,013 RGB images, each resized to a resolution of 200×200 pixels. Preprocessing techniques, including data augmentation methods such as rotation, shifting, and zoom, are applied to enhance the dataset’s diversity and normalize pixel values for consistent model training.The CNN model architecture incorporates multiple convolutional layers, max-pooling layers, and dropout mechanisms for regularization, culminating in a fully connected layer with a sigmoid activation function for binary classification (benign or malignant). The training process utilizes a batch size of 32 images and incorporates optimization techniques such as early stopping, learning rate adjustment, and model checkpointing to enhance performance. Achieving an accuracy of 90.3%, the proposed system significantly outperforms the 78% accuracy commonly associated with traditional machine learning approaches. These findings highlight the superior capabilities of CNN-based models in breast cancer detection, reducing the likelihood of diagnostic errors. This study demonstrates that CNN-based approaches can substantially improve the accuracy and reliability of early breast cancer detection systems, offering a valuable tool for clinical diagnostics.
Keywords	Breast Cancer Detection, Histopathological Images, Convolutional Neural Network (CNN), Computer-Aided Diagnosis (CAD), Medical Image Analysis, Deep Learning, Image Classification, BreakHis Dataset, Data Augmentation, Malignant and Benign Tumors, Early Cancer Diagnosis, Image Preprocessing, Feature Extraction, Diagnostic Accuracy, Artificial Intelligence in Healthcare
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-05-12
DOI	https://doi.org/10.71097/IJSAT.v16.i2.4732
Short DOI	https://doi.org/g9kc7k

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Histopathological Image Analysis For Breast Cancer

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