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@article{187233,
        author = {Kiran A. Sable and Ugale Vrushali Balasaheb and Sawant Pratiksha Pramod and Suryawanshi Payal Satish and Mehta Janhvi Parshram},
        title = {Review on Breast Cancer Classification using Convolutional Neural Network (CNN)},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {5612-5621},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187233},
        abstract = {Breast cancer remains one of the most common and life-threatening diseases affecting women across the world. The effectiveness of treatment largely depends on early and accurate diagnosis. However, manual analysis of histopathological images by pathologists can be challenging, subjective, and prone to human error. With the rapid growth of artificial intelligence and deep learning, automated systems have shown strong potential in assisting medical experts by improving diagnostic accuracy and reducing workload.

This study presents an enhanced Convolutional Neural Network (CNN) model for breast cancer image classification using the publicly available BreakHis dataset. The dataset consists of microscopic images of breast tumor tissues categorized as benign or malignant across four magnification levels (40×, 100×, 200×, and 400×). The proposed CNN architecture has been designed to capture complex spatial features by optimizing convolutional layers and incorporating adaptive learning mechanisms to ensure better generalization. Preprocessing techniques such as image normalization and contrast enhancement are applied to refine the image quality and highlight important tissue characteristics.},
        keywords = {Breast Cancer, Deep Learning, (CNN), BreakHis Dataset, etc.},
        month = {November},
        }