An Efficient and Lightweight Convolutional Neural Network Model for Lung Cancer Diagnosis

Year 2026, Volume: 22 Issue: 1 , 95 - 105 , 30.03.2026

Çimen Uğur , Mahir Kaya

https://doi.org/10.18466/cbayarfbe.1620394

https://izlik.org/JA55YR32UW

Abstract

Lung cancer stands as a major driver of mortality among cancer patients on a global scale, poses a substantial public health challenge due to its late diagnosis and the ambiguity of its early symptoms. This study proposes a novel, lightweight, and optimized Convolutional Neural Network architecture for detecting lung cancer types from Computed Tomography (CT) images. Initially, CT images of various lung cancer patients were combined for binary classification against normal patients. Experiments using different architectures and systematic hyperparameter optimizations resulted in the best model achieving a 93% accuracy rate. In the next phase, multi-class classification tasks were performed on the original dataset, and the performance of optimization algorithms such as Adamax, Adam, RMSprop, SGD, Adadelta, Nadam, and Adagrad were compared to determine the best optimizer. In these experiments, Adam algorithm achieved an 87% accuracy. The same study was repeated on an augmented dataset using data augmentation methods, reaching an accuracy of up to 96% with the RMSprop optimization algorithm. Furthermore, fine-tuned models were used and their test accuracies were evaluated using transfer learning methods (VGG16, ResNet50, InceptionV3, DenseNet121) on the available dataset. The primary contribution of this study is demonstrating that the proposed lightweight, optimized model achieves higher performance and efficiency on task-specific datasets, outperforming both established transfer learning methods and existing literature in accuracy. The proposed model will help healthcare professionals make quick and reliable decisions in lung cancer diagnoses.

Keywords

Convolutional Neural Networks , Lung Cancer , Transfer Learning , Hyperparameter Optimizations , Computed Tomography

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