STM Journals Peer-reviewed research from Consortium e-Learning Network

Research and Reviews: Journal of Oncology and Hematology Original Research

Early Lung Cancer Prediction using deep Learning

  1. Mohit Bharat Vishwakarma MCA, Thakur Institute of Management Studies, Career Development & Research, Mumbai
  2. Karan Gajajibhai Vala MCA, Thakur Institute of Management Studies, Career Development & Research, Mumbai
  3. Padma Mishra MCA, Thakur Institute of Management Studies, Career Development & Research, Mumbai
  4. Shubham Mishra MCA, Thakur Institute of Management Studies, Career Development & Research, Mumbai

Abstract

Lung cancer is a global killer because it’s often found late. Finding it early is key to treatment and survival so computer assisted diagnostics are essential. This research uses deep learning to spot early stage lung cancer from CT scans. We trained and fine-tuned three convolutional neural networks—ResNet50, Dense Net 201 and EfficientNet-B0—using transfer learning. We preprocessed the lung CT images by resizing, normalizing and augmenting them to enhance the models and prevent overfitting. Training was optimized for limited computing power. ResNet50 was good with reasonable complexity. DenseNet201 was good at extracting deep features so classification was better. EfficientNet-B0 was a streamlined and effective option for quick screening. These models look promising to help radiologists with automated, precise and scalable diagnostic support. Adding explainability tools like Grad-CAM will make the results more interpretable and build trust in these deep learning systems. Next steps are to test on different groups, with bigger datasets and deploy streamlined architectures in real world clinical settings.

Keywords

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