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@article{177359,
        author = {Sanket Sawarkar and Akshay Gite and Sujal Singh and Lucky Baghele and Yashwant Ingle},
        title = {Lung Cancer Detection Using Inception V3},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1813-1820},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177359},
        abstract = {Lung cancer is one of the leading causes of death globally [1], making early and accurate detection critical for improving patient outcomes. This study presents a deep learning-based approach for the automatic classification of lung cancer using the Inception V3 convolutional neural network (CNN) architecture [3]. The model employs transfer learning to adapt a pretrained Inception V3 network for lung cancer classification, fine-tuned on a custom dataset of 15,000 CT scan images distributed equally among three diagnostic categories: adenocarcinoma, benign, and squamous cell carcinoma.
The dataset was divided into training and validation sets in an 80:20 ratio. Extensive data augmentation techniques were applied to improve generalization and minimize overfitting [8], [11]. The model was trained for 20 epochs, and its performance was evaluated using standard metrics such as accuracy, loss, precision, recall, and F1-score. The proposed system achieved a validation accuracy of 95.97% and a validation loss of 0.0986, demonstrating high effectiveness in classifying lung cancer types from CT scans. The results align with recent advancements in CNN-based medical image classification [2], [9], [14], [15], reinforcing the suitability of Inception V3 for clinical decision support systems in oncology.},
        keywords = {Lung cancer detection, Convolutional Neural Networks (CNN), Inception V3 [3], CT scan classification, image-based diagnosis, malignant tumors, benign tumors, transfer learning.},
        month = {May},
        }