(KAUH-BCMD) Dataset: Advancing Mammographic Breast Cancer Classification with Multi-Fusion Preprocessing and Residual Depthwise Network

Asma’a Mohammad Al-Mnayyis ¹ Hasan Gharaibeh‬‏ ^1* Khalid M.O. Nahar ² Duha Anakreh ³ Hanan Fawaz Akhdar ^4* Eman Hussein Alshdaifat ¹ Mohammad Amin ¹ Ahmad Nasayreh ¹ Mohammad Gharaibeh ⁵ Neda'a Alsalmanc ³ Alaa Alomar ³ Maha Gharaibeh ³ Hamad Yahia Abu Mhanna ⁶

• ¹ Yarmouk University, Irbid, Jordan
• ² Arab Open University, Riyadh, Saudi Arabia
• ³ Jordan University of Science and Technology, Irbid, Irbid, Jordan
• ⁴ Imam Muhammad ibn Saud Islamic University, Riyadh, Saudi Arabia
• ⁵ Hashemite University, Zarqa, Zarqa, Jordan
• ⁶ University of Science Malaysia (USM), Penang, Penang, Malaysia

The categorization of benign and malignant patterns in digital mammography is a critical step in the diagnosis of breast cancer, facilitating early detection and potentially saving many lives. Diverse breast tissue architectures often obscure and conceal breast issues. Classifying worrying regions (benign and malignant patterns) in digital mammograms is a significant challenge for radiologists. Even for specialists, the first visual indicators are nuanced and irregular, complicating identification. Therefore, radiologists want an advanced classifier to assist in identifying breast cancer and categorizing regions of concern. This study presents an enhanced technique for the classification of breast cancer using mammography images. The collection comprises real-world data from King Abdullah University Hospital (KAUH) at Jordan University of Science and Technology, consisting of 7,205 photographs from 5,000 patients aged 18 to 75. After being classified as benign or malignant, the pictures underwent preprocessing by rescaling, normalization, and augmentation. Multi-fusion approaches, such as highboost filtering and contrast-limited adaptive histogram equalization (CLAHE), were used to improve picture quality. We created a unique Residual Depth-wise Network (RDN) to enhance the precision of breast cancer detection. The suggested RDN model was compared with many prominent models, including MobileNetV2, VGG16, VGG19, ResNet50, InceptionV3, Xception, and DenseNet121. The RDN model exhibited superior performance, achieving an accuracy of 97.82%, precision of 96.55%, recall of 99.19%, specificity of 96.45%, F1 score of 97.85%, and validation accuracy of 96.20%. The findings indicate that the proposed RDN model is an excellent instrument for early diagnosis using mammography images and significantly improves breast cancer detection when integrated with multifusion and efficient preprocessing approaches.