A novel breast cancer detection system using SDM-WHO-RNN classifier with LS-CED segmentation

Abstract

Breast cancer (BC) is caused by the abnormal and rapid growth of breast cells. Accurate diagnosis of BC at an early stage could minimize the mortality related to this disease. Because of this, researchers have recently developed an automatic recognition system. However, poor image resolution, lower accuracy for deep tumors, biased nature and incapability to localize a lesion, greater rate of False Positives (FPs), and inefficiency towards the overlapped cells are the restrictions encompassed in the majority of the prevailing techniques. The main motive of this proposed work is for detecting the nuclei and classifying the tumor. Finding the nuclei's location is the main goal of the proposed endeavour. The structure of the cell is measured in relation to the nuclei. Hence, superior accuracy in complexity classification could be attained by this incentive. The input image is preprocessed and the contrast is enhanced. By utilizing Linear Scaling centered Canny Edge Detection (LS-CED), these Contrast-Enhanced Images (CEI) are further segmented into several partitions. The nuclei are sensed by utilizing a Soft Plus-Max Region-centered Fully Convolutional Network (SPM-R-FCN) from the segmented images. Then, as per the size and shape of the tumor, the Tumor Cells (TCs) are gauged and classified by utilizing the adaptive threshold function. After that, the most momentous and needed features are extorted as of the gauged TCs. This Extracted Feature (EF) is inputted into the SDM-WHO-RNN classifier that classifies cancer as benign, malignant, and normal. Simulation results demonstrated that the SDM-WHO-RNN approach acquires perfect outcomes as of the experiment and accomplishes 97.9% accuracy.