Genetically Optimized Modular Neural Networks for Precision Lung Cancer Diagnosis: Exploratory Study of Novel Approach.

Background/Aim: Lung cancer is one of the leading causes of cancer deaths. While low-dose computed tomography (CT) screening improves survival, radiological detection is increasingly challenged by a shortage of radiologists. This study aimed to develop and evaluate a novel, precise, and computationally efficient AI-based algorithm for lung cancer diagnosis using chest CT scans. Patients and Methods: A total of 156 patient chest CT scans were utilized to form Databases I and II. We then conducted extensive feature extraction [statistics, histograms, Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), Walsh-Hadamard Transform (WHT)] and optimized classifiers [Multi Layer Perceptron (MLP), Generalized Feed Forward Neural Network (GFF-NN), Modular Neural Network (MNN), Support Vector Machine (SVM)] with genetic algorithms. Performance evaluation measures employed were classification accuracy, Mean Squared Error (MSE), Area under the ROC curve (AUC), and computational efficiency. Results: The MNN (Topology II) classifier employing FFT-based features with momentum learning achieved 100% classification accuracy during cross-validation for both Database I and Database II, consistently yielding perfect average classification accuracy across both datasets. Conclusion: The genetically optimized MNN (Topology II) classifier shows remarkable performance in lung cancer diagnosis from CT scan images. Its ability to achieve perfect classification accuracy suggests strong potential for clinical application, offering both diagnostic precision, acting as a triage, and workload reduction in healthcare settings.