Journal Article

Breast-NET: a lightweight DCNN model for breast cancer detection and grading using histological samples

Das, Deepanwita, Chakraborty, Mainak, Saha, Mousumi, Maiti, Suchismita

2024

Overview

Breast cancer is a prevalent and highly lethal cancer affecting women globally. While non-invasive techniques like ultrasound and mammogram are used for diagnosis, histological examination after biopsy is considered the gold standard. However, manual examination of tissues for abnormality is labor-intensive, expensive, and requires prior domain knowledge. Early detection, awareness, and access to specialized medical infrastructure in resource-constrained and remote areas are significant challenges but crucial for saving lives. In recent years, deep learning-based approaches have shown promising results in breast cancer detection, facilitated by advancements in GPU memory, computation power, and the availability of digital data. Motivated by these observations, we propose the Breast-NET deep convolutional neural network model for breast cancer detection and grading using histological images. Our model’s performance is evaluated on the BreakHis dataset, and we demonstrate its generalization ability on the Invasive Ductal Carcinoma (IDC) grading and IDC datasets. Extensive experimental and statistical performance analysis, along with an ablation study, validates the efficiency of our proposed model. Furthermore, we demonstrate the effectiveness of transfer learning with seven pre-trained convolutional neural networks for breast cancer detection and grading. Experimental results show that our framework outperforms state-of-the-art approaches in terms of accuracy, space, and computational complexity for the BreakHis, IDC grading, and IDC datasets.

• Publisher: Springer London,Springer Nature B.V
• Subject: Ablation / Artificial Intelligence / Artificial neural networks / BreakHis / Breast cancer / Breast cancer detection / Breast cancer grading / Breast-NET / Computational Biology/Bioinformatics / Computational Science and Engineering / Computer Science / Convolution / Convolutional neural networks / Data Mining and Knowledge Discovery / Datasets / Deep learning / Deep neural networks / Diagnosis / Digital data / Digital imaging / Diseases / Domain Knowledge / Grading / IDC / IDC grading / Image Processing and Computer Vision / Invasive ductal carcinoma grading / Invasive ductal carcinomata / Mammography / Medical imaging / Neural networks / Original Article / Performance evaluation / Probability and Statistics in Computer Science / Transfer learning / Ultrasonic applications / Ultrasonic testing