An Improved Deep CNN for Early Breast Cancer Detection

Over the past several decades, breast cancer has emerged as one of the most devastating illnesses globally. Globally, breast cancer is the second highest cause of mortality among all forms of cancer. Early detection, which permits the total elimination of cancer by surgery or treatment, is one of the most efficient techniques for treating cancer. Thermography, ultrasonography, and mammography are among the different technologies created for the goal of breast cancer screening. Utilizing image processing and deep learning methods, this technology can boost the radiologist’s ability to effectively detect chest anomalies. This study advises upgrading the breast cancer detection approach using a Deep Convolutional Neural Network (DCNN) to offer precise and quick findings. Furthermore, this study separates itself from the previous one by adopting a DCNN with 12 stacked processing layers. The implementation of a 12-layered Convolutional Neural Network (CNN) considerably enhanced the precision of breast cancer diagnosis and detection. We applied the Mini Mammographic Database (MIAS) to examine the efficacy of the suggested technique. Nevertheless, the acquired data reveals that Deep CNN attained a spectacular accuracy rate of 99.1%, resulting in excellent consequences. In addition, the DPD-DCNN achieved the greatest degree of accuracy when compared to similar trials.