Improvement in Validation Score with Loss Function for Breast Cancer Detection Using Deep Learning

Background: The most often type of cancer detected in women is Breast cancer. It is formed in the breast cells. It is an acutely life-threatening disease in women as compared to other cancers. Cancer caused in the breast is grouped into Various categorizations of breast cancer as per their cell's appearance (i) invasive ductal carcinoma (IDC) and (ii) ductal carcinoma in situ (DCIS),
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one being generally having no negative effects and is formed quite slowly, whereas the 1st one, IDC type is comparatively more dangerous and surrounds the breast tissues to great extent. Around 80% of the breast cancer patients fall under this category [1]. Scope: In the context of medical data, where comprehensive information about breast cancer and its symptoms is available, the scope of our research becomes especially relevant. This field requires efficient methods for the detection of the disease to support healthcare professionals in delivering effective diagnoses. Problem: The fundamental challenge lies in the need for timely and precise patient disease detection summaries during the diagnostic process. This problem has been a longstanding concern in the healthcare industry. The core issue at hand is the inefficiency and resource burden posed by the current manual methods of summarizing medical data during diagnosis. These methods are neither time-effective nor cost-efficient, often leading to delays in patient care and a drain on valuable resources. Overall Contribution: Our proposed method aims to Improve the validation score with loss function for breast cancer detection. This innovation will significantly benefit the research community and healthcare experts by streamlining the process of diagnosis. By saving time and resources, our contribution promises to enhance the overall efficiency of healthcare practices, leading to more effective and timely patient care.