JetNet: An Effective Deep Learning Model for Histopathological Lung Cancer Classification and Diagnosis

Abstract

Effectively distinguishing lung cancer subtypes through histopathological imaging plays a vital role in accelerating diagnosis and guiding appropriate treatment strategies. Deep learning techniques, particularly convolutional neural networks (CNNs), have demonstrated remarkable success in medical image analysis. However, many state-of-the-art CNN architectures such as DenseNet, EfficientNet, and MobileNetV2 require substantial computational resources, limiting their clinical deployment in resource-constrained environments. In this study, we propose \textbf{JetNet}, a novel CNN architecture designed to deliver both high classification accuracy and computational efficiency. JetNet incorporates a streamlined sequence of convolutional layers, batch normalization, global average pooling, and dropout regularization, resulting in a lightweight model with significantly fewer parameters. Evaluated on a publicly available histopathological lung cancer dataset, JetNet achieved an accuracy of 99.6%, outperforming well-established models including DenseNet, EfficientNet, and MobileNetV2. The proposed model’s balance of performance and efficiency makes it particularly suitable for real-time diagnostic applications and deployment in clinical settings with limited computational infrastructure. This work advances automated lung cancer diagnosis and supports improved clinical decision-making.