Breast Cancer Diagnosis: Deep Learning Advances in Invasive Ductal Carcinoma Detection
Kolla Bhanu Prakash · 2024
Breast cancer remains one of the most prevalent and life-threatening diseases affecting women worldwide. Invasive Ductal Carcinoma (IDC) is the most common type of breast cancer, comprising a significant portion of diagnoses. Early and accurate detection of IDC is crucial for effective treatment and improved patient outcomes. In recent years, deep learning, a subset of artificial intelligence, has emerged as a promising tool in medical image analysis, particularly in breast cancer diagnosis. This paper explores the recent advances in deep learning techniques for IDC detection, focusing on the development of robust algorithms capable of accurately identifying IDC from mammographic images and histopathological slides. We review various deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants, highlighting their strengths and limitations in IDC detection. Additionally, we discuss the challenges associated with implementing deep learning models in clinical practice, such as data scarcity, interpretability, and regulatory considerations. Despite these challenges, deep learning holds great potential to revolutionize breast cancer diagnosis by enabling earlier detection, personalized treatment strategies, and ultimately, improved patient outcomes.