Leveraging medical imaging and deep learning for diagnosis of breast cancer using histopathological images

Globally, breast cancer is one the life-threatening diseases among women. Breast histopathological image (HI) investigation is the most effective method applied for the recognition of cancer malignancy. Manual breast HI investigation is, still, time-consuming, abstract, and susceptible to human errors. Computer-aided diagnosis (CAD) became a possible and popular solution for medicinal image investigation owing to current developments in computer memory and power. Still, the CAD methods performance required to be enhanced to apply for practical reasons. In recent times, deep learning (DL) like convolution neural networks (CNN) have proved reliable in identifying BC targets from pathological images. This paper proposes a Leveraging Medical Imaging for Early Breast Cancer Detection Using Deep Leaning (LMIBCD-DL). The LMIBCD-DL approach presents a reliable tool for supporting automated breast cancer diagnosis in medical applications. Initially, the LMIBCD-DL method applies wiener filtering (WF) for the image preprocessing step to effectively reduce noise while preserving important tissue structures. For feature extractors, the squeeze-and-excitation ResNet (SE-ResNet) model is employed to capture rich and discriminative representations from complex histopathological patterns. Finally, the bi-directional long short-term memory (BiLSTM) is utilized for the BC detection process. The comparative results demonstrated a superior accuracy value of 98.72% over the existing methodologies under dissimilar measures and the BreakHis dataset.