Early Lung Cancer Prediction using deep Learning

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 15 | Issue : 02 | Page :
    By

    Mohit Bharat Vishwakarma,

  • Karan Gajajibhai Vala,

  • Padma Mishra,

  • Shubham Mishra,

  1. Research Scholar, MCA, Thakur Institute of Management Studies, Career Development & Research, Mumbai, Maharashtra, India
  2. Research Scholar, MCA, Thakur Institute of Management Studies, Career Development & Research, Mumbai, Maharashtra, India
  3. Associate Professor, MCA, Thakur Institute of Management Studies, Career Development & Research, Mumbai, Maharashtra, India
  4. Assistant Professor, MCA, Thakur Institute of Management Studies, Career Development & Research, Mumbai, Maharashtra, India

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: Early lung cancer detection, Deep learning, Convolutional neural networks, ResNet50, DenseNet201, EfficientNet-B0, Medical imaging.

[This article belongs to Research and Reviews: Journal of Oncology and Hematology ]

How to cite this article:
Mohit Bharat Vishwakarma, Karan Gajajibhai Vala, Padma Mishra, Shubham Mishra. Early Lung Cancer Prediction using deep Learning. Research and Reviews: Journal of Oncology and Hematology. 2026; 15(02):-.
How to cite this URL:
Mohit Bharat Vishwakarma, Karan Gajajibhai Vala, Padma Mishra, Shubham Mishra. Early Lung Cancer Prediction using deep Learning. Research and Reviews: Journal of Oncology and Hematology. 2026; 15(02):-. Available from: https://journals.stmjournals.com/rrjooh/article=2026/view=248891


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Regular Issue Subscription Original Research
Volume 15
Issue 02
Received 14/03/2026
Accepted 11/06/2026
Published 03/07/2026
Publication Time 111 Days
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