Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques

• Published in: BMC medical informatics and decision making Vol. 24; no. 1; p. 142
• Main Authors: Musthafa, M Mohamed, Manimozhi, I, Mahesh, T R, Guluwadi, Suresh
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 27.05.2024; BioMed Central; BMC
• Subjects: Accuracy; Artificial neural networks; Automation; Biopsy; Blurring; Cancer; Cancer therapies; Classification; Clinical outcomes; CNN; Computed tomography; CT scans; Datasets; Deep Learning; Diagnosis; Health aspects; Humans; Image enhancement; Image preprocessing; Image Processing, Computer-Assisted - methods; Invasiveness; Learning algorithms; Literature reviews; Lung cancer; Lung Neoplasms - classification; Lung Neoplasms - diagnostic imaging; Machine Learning; Medical diagnosis; Medical imaging; Medical prognosis; Mortality; Neural networks; Neural Networks, Computer; Recall; SMOTE; Tomography, X-Ray Computed; Tumors; X-rays; India; CNN; SMOTE; Diagnostic accuracy; Lung cancer; Classification; Machine learning; Image preprocessing; CT scans
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-024-02553-9

Lung cancer remains a leading cause of cancer-related mortality globally, with prognosis significantly dependent on early-stage detection. Traditional diagnostic methods, though effective, often face challenges regarding accuracy, early detection, and scalability, being invasive, time-consuming, and prone to ambiguous interpretations. This study proposes an advanced machine learning model designed to enhance lung cancer stage classification using CT scan images, aiming to overcome these limitations by offering a faster, non-invasive, and reliable diagnostic tool. Utilizing the IQ-OTHNCCD lung cancer dataset, comprising CT scans from various stages of lung cancer and healthy individuals, we performed extensive preprocessing including resizing, normalization, and Gaussian blurring. A Convolutional Neural Network (CNN) was then trained on this preprocessed data, and class imbalance was addressed using Synthetic Minority Over-sampling Technique (SMOTE). The model's performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and ROC curve analysis. The results demonstrated a classification accuracy of 99.64%, with precision, recall, and F1-score values exceeding 98% across all categories. SMOTE significantly enhanced the model's ability to classify underrepresented classes, contributing to the robustness of the diagnostic tool. These findings underscore the potential of machine learning in transforming lung cancer diagnostics, providing high accuracy in stage classification, which could facilitate early detection and tailored treatment strategies, ultimately improving patient outcomes.