A LIGHTWEIGHT CONVOLUTIONAL NEURAL NETWORK FOR BREAST CANCER DETECTION USING KNOWLEDGE DISTILLATION TECHNIQUES

A LIGHTWEIGHT CONVOLUTIONAL NEURAL NETWORK FOR BREAST CANCER DETECTION USING KNOWLEDGE DISTILLATION TECHNIQUES

Abstract:
Breast cancer is one of the most prevalent and life-threatening diseases among women worldwide. Early detection plays a crucial role in improving patient outcomes and survival rates. Convolutional Neural Networks (CNNs) have shown remarkable success in various medical image analysis tasks, including breast cancer detection. However, the deployment of CNNs on resource-constrained devices such as smartphones or embedded systems remains a challenge due to their high computational requirements.

In this research, we propose a lightweight Convolutional Neural Network architecture for breast cancer detection. Our architecture is designed to achieve a balance between model complexity and performance, making it suitable for deployment on resource-limited platforms. To further enhance the efficiency of our network, we employ knowledge distillation techniques.

Knowledge distillation is a process where a smaller student network is trained to mimic the behavior of a larger, more complex teacher network. By distilling the knowledge from the teacher network, we aim to transfer its high-level representations and decision-making capabilities to the lightweight student network, thereby reducing the model size and computational requirements without significant loss in performance.

We conduct extensive experiments on a publicly available breast cancer dataset, evaluating the performance of our proposed lightweight CNN architecture with and without knowledge distillation. The experimental results demonstrate that our lightweight network, combined with knowledge distillation, achieves competitive accuracy compared to larger, more computationally expensive models. Moreover, our network demonstrates superior efficiency in terms of model size and computational requirements, making it suitable for real-time breast cancer detection applications on resource-constrained devices.

In conclusion, our research presents a lightweight CNN architecture for breast cancer detection that addresses the challenges of resource-constrained environments. By leveraging knowledge distillation techniques, we demonstrate that it is possible to achieve a compact and efficient model while maintaining competitive accuracy. The proposed network has the potential to contribute to early detection and improve the accessibility of breast cancer screening in various healthcare settings.

A LIGHTWEIGHT CONVOLUTIONAL NEURAL NETWORK FOR BREAST CANCER DETECTION USING KNOWLEDGE DISTILLATION TECHNIQUES. GET MORE  COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS

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