Breast Cancer Identification using Hybrid Algorithm
Keywords:
Breast Cancer, Convolutional Neural Network, Soft Convolution Grad CAM, Feature ExtractionAbstract
The Soft Computing has the prospective to predict diseases based on features buried in data. Incidence and humanity rates from breast cancer have risen steadily during the previous three eras. In 2023 it is estimated 3,00,590 people were diagnosed with breast cancer. Around 2,97,790 new cases are diagnosed in women at every month. By 2030, experts predict that the annual number of new cases analysed will have reached 2.7 million with 0.87 million deaths. This breast cancer caused by many factors like various Clinical, Social, Lifestyle and Economic. So key challenge of predicting the breast cancer is the construction of prototype for addressing all notorious risks factors. The feature extraction will improve the predictive performance of a model with Convolutional Neural Network (CNN). This will retain a new recognition task based on existing network with trained weights. In addition, this model will improve the quality of extraction so it makes the best choice for analysis. In this article, hybrid method Convolutional Neural Network (CNN) with Deep feature extraction method i.e., Soft Convolutional Grad-CAM (SCGC) method is proposed to identify the breast cancer tumor along with to know whether cancer is in nodes of lymph or spread to other parts of the body.
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