J.V. G. Prakasa Rao Pyla , D. Mabuni 3 3 (July - September) Lung cancer remained one of the leading causes of cancerrelated mortality worldwide making early diagnosis essential for improving treatment opportunities and patient outcomes Computed tomography CT imaging became an important diagnostic modality because of its ability to capture detailed anatomical information from the pulmonary region At the same time advances in artificial intelligence and deep learning created opportunities for automated medical image analysis and decision support Despite this progress challenges including limited labelled datasets moderate generalisation capability computational constraints reproducibility concerns and limited interpretability continued to influence the development of reliable diagnostic frameworks This research developed a transfer learning framework based on ResNet50 for lung cancer detection using CT scan images evaluated classification performance through accuracy precision recall and F1score and established a reproducible experimental baseline for future explainable and optimised diagnostic systems A publicly available CT image dataset containing four diagnostic categories was utilised Data preparation included image resizing normalisation and augmentation before model training The experimental outcomes demonstrated progressive learning behaviour throughout training where training accuracy reached 7132 and validation accuracy reached 6250 accompanied by decreasing training and validation loss values Evaluation on the testing dataset produced an overall classification accuracy of 57 with weighted precision recall and F1score values of 057 057 and 050 respectively Classlevel analysis indicated stronger recognition performance for normal CT images and comparatively balanced detection capability for adenocarcinoma whereas lower performance was observed for large cell carcinoma and squamous cell carcinoma The findings suggested that transfer learning remained a practical approach for CTbased lung cancer classification under limited data conditions In addition to experimental evaluation the study contributed a reproducible implementation framework that may support future comparison explainability integration and continued optimisation of diagnostic models Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026