Development of MobileNetV2 for CT-Scan Lung Classification Using Transfer Learning - Rajendra, Daffa Fadhil; Wardhana, Ajie Kusuma (Journal of Applied Informatics and Computing, 2025)
Lung cancer is one of the leading causes of cancer-related deaths worldwide, making early detection crucial for improving patient survival rates. This study proposes an automated classification approach based on deep learning using the MobileNetV2 architecture to identify three categories of lung CT scan images: normal, benign, and malignant. The dataset used is the augmented IQ-OTH/NCCD Lung Cancer Dataset, consisting of 3,609 images with a resolution of 224×224 pixels. All images underwent preprocessing steps including RGB conversion, pixel rescaling, and normalization. The MobileNetV2 model was modified by adding a GlobalAveragePooling2D layer, a dense layer, and dropout to reduce overfitting risk. Training was conducted for 28 epochs using the optimizer Adam, followed by evaluation using accuracy, precision, recall, and F1-score metrics. The model was tested on unseen data and validated using Stratified 5-Fold Cross Validation. The testing results showed an overall accuracy of 97%, with a perfect recall score (1.00) for the malignant class. The cross-validation yielded an average accuracy of 97.26% with a standard deviation of ±0.66%, indicating consistent model performance. Given its lightweight architecture and high accuracy, MobileNetV2 has the potential to be implemented as a decision support system in medical image analysis.