Authors:
Agnesh Yadav
1
;
Maheshkumar Kolekar
1
and
Mukesh Zope
2
Affiliations:
1
Department of Electrical Engineering, IIT Patna, Patna, India
;
2
Department Medical Physics, IGIMS Patna, Patna, India
Keyword(s):
Breast Cancer Classification, Ultrasound Images, Deep Learning, ResNet-101.
Abstract:
In the modern era, accurate breast cancer classification plays a crucial role in early detection and treatment planning. This article introduces a modified ResNet-101 architecture tailored specifically for classifying breast cancer using ultrasound images. The ultrasound images undergo pre-processing before passing through our adapted ResNet-101 model, which includes the integration of shortcut connections to enhance gradient stability and deep structure adaptability for effective learning and classification. The dataset comprises 780 images categorized into normal, benign, and malignant cases. To address class imbalance, data augmentation techniques are employed, enriching diversity and enhancing modeling precision. The proposed model achieves exceptional performance, boasting precision, recall, F1-score, and accuracy values of 0.9855, 0.9677, 0.9756, and 0.9743, respectively. The comparative analysis highlights the superiority of our model over existing techniques. Furthermore, we
explore its potential for clinical application using real-world datasets. Our findings indicate significant promise in revolutionizing breast cancer detection, offering a robust tool for early and accurate diagnosis with the potential to impact patient outcomes greatly.
(More)