DCSNet: A Lightweight Knowledge Distillation-Based Model with Explainable AI for Lung Cancer Diagnosis from Histopathological Images

TL;DR

This paper introduces DCSNet, a lightweight, knowledge distillation-based CNN model with explainable AI techniques for accurate and transparent lung cancer diagnosis from histopathological images, suitable for resource-limited settings.

Contribution

It presents a novel lightweight model trained via knowledge distillation from large CNNs, incorporating explainability to improve trust and applicability in healthcare diagnostics.

Findings

DCSNet achieves high diagnostic accuracy comparable to larger models.

The model demonstrates improved transparency through explainable AI techniques.

Resource efficiency makes DCSNet suitable for deployment in constrained environments.

Abstract

Lung cancer is a leading cause of cancer-related deaths globally, where early detection and accurate diagnosis are critical for improving survival rates. While deep learning, particularly convolutional neural networks (CNNs), has revolutionized medical image analysis by detecting subtle patterns indicative of early-stage lung cancer, its adoption faces challenges. These models are often computationally expensive and require significant resources, making them unsuitable for resource constrained environments. Additionally, their lack of transparency hinders trust and broader adoption in sensitive fields like healthcare. Knowledge distillation addresses these challenges by transferring knowledge from large, complex models (teachers) to smaller, lightweight models (students). We propose a knowledge distillation-based approach for lung cancer detection, incorporating explainable AI (XAI) techniques to enhance model transparency. Eight CNNs, including ResNet50, EfficientNetB0, EfficientNetB3, and VGG16, are evaluated as teacher models. We developed and trained a lightweight student model, Distilled Custom Student Network (DCSNet) using ResNet50 as the teacher. This approach not only ensures high diagnostic performance in resource-constrained settings but also addresses transparency concerns, facilitating the adoption of AI-driven diagnostic tools in healthcare.