Progressing Breast Cancer Assessment: Precise Tumor Categorization through DenseNet201-based Deep Learning
K. Logu, S. John Justin Thangaraj · 2024
In this groundbreaking research endeavor, we present a novel approach to breast cancer assessment, leveraging the power of deep learning and transfer learning techniques. Our methodology involves the fine-tuning of a pre-trained DenseNet201 model using the extensive BreakHis dataset, aiming to achieve precise categorization of breast cancer tumors. The primary objective of our study is to enhance the accuracy and reliability of breast cancer diagnosis through the utilization of state-of-the-art deep learning architectures. Employing transfer learning, we fine-tuned the pre-trained DenseNet201 model on the BreakHis dataset, a comprehensive and diverse collection of breast histopathological images. This dataset encompasses various benign and malignant breast tumor cases, providing a robust foundation for our model to learn intricate patterns and features. During the training phase, our model exhibited remarkable performance, achieving an impressive accuracy of 97.00%. The validation phase further reinforced the model's capabilities, yielding a validation accuracy of 92.00%. These compelling results underscore the efficacy of our approach in accurately categorizing breast tumors, thereby contributing to the advancement of breast cancer diagnostics. This research not only showcases the potential of deep learning in the field of medical image analysis but also emphasizes the importance of leveraging transfer learning to optimize model performance. The ability to discern subtle patterns in histopathological images enables our model to provide clinicians with reliable information for more accurate and timely breast cancer diagnosis. Our study signifies a significant step forward in the ongoing efforts to improve breast cancer assessment methodologies, with potential implications for enhancing patient outcomes through early and precise detection. The integration of advanced technologies, such as deep learning, into medical diagnostics holds promise for revolutionizing the way we approach and combat critical diseases, contributing to a more efficient and effective healthcare landscape.