Lung Cancer Detection and Classification Using Deep Learning

Year : 2024 | Volume :14 | Issue : 03 | Page : –
By
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Rohan Kenjale,

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Aparna R. Lokhande,

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Siddhesh Lohar,

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Harshada Gaikwad,

  1. Student, Department of Electronics and Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Vadgaon Bk, Pune, Maharashtra, India
  2. Student, Department of Electronics and Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Vadgaon Bk, Pune, Maharashtra, India
  3. Student, Department of Electronics and Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Vadgaon Bk, Pune, Maharashtra, India
  4. Assistant Professor, Department of Electronics and Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Vadgaon Bk, Pune, Maharashtra, India

Abstract document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_114038’);});Edit Abstract & Keyword

Lung cancer is a disease that can be effectively treated if detected early. Various technologies, such as magnetic resonance imaging, isotopes, X-rays, and computed tomography scans, are employed for diagnosis. One of the most crucial strategies in combating cancer is early detection, which greatly enhances a patient’s likelihood of survival; this is where artificial intelligence plays a significant role. The approach proposed in this study leverages historical medical data to assess a patient’s likelihood of having lung cancer. It employs a convolutional neural network (CNN) to analyze computed tomography scans and ascertain the cancer stage. Accurately diagnosing the disease and determining its stage at an early stage can be lifesaving for patients. Although multiple methods, including image processing, biomarker analysis, and machine learning, are available for lung cancer detection, achieving high accuracy and timely diagnosis remains a challenge for healthcare professionals. This study uses data from the Lung Image Database Consortium and the Image Database Resource Initiative (LIDC-IDRI) to extract computed tomography images. In traditional approaches, manual examination of computed tomography images is necessary to determine if a patient has lung cancer. Overall, the primary goal of using deep learning for lung cancer detection is to enhance the accuracy and efficiency of diagnosis, improve patient outcomes, and contribute to ongoing medical research in the field of oncology. The effectiveness of using convolutional neural networks algorithms in digital pathology image processing, as previously described, and intends to further discover the high level and discriminative properties shown by cancer cells using convolutional neural networks for exact categorization of lung cancer subtype.

Keywords: Lung cancer, CT scan, CNN, deep learning, image processing

[This article belongs to Research & Reviews: Journal of Oncology and Hematology (rrjooh)]

How to cite this article:
Rohan Kenjale, Aparna R. Lokhande, Siddhesh Lohar, Harshada Gaikwad. Lung Cancer Detection and Classification Using Deep Learning. Research & Reviews: Journal of Oncology and Hematology. 2024; 14(03):-.
How to cite this URL:
Rohan Kenjale, Aparna R. Lokhande, Siddhesh Lohar, Harshada Gaikwad. Lung Cancer Detection and Classification Using Deep Learning. Research & Reviews: Journal of Oncology and Hematology. 2024; 14(03):-. Available from: https://journals.stmjournals.com/rrjooh/article=2024/view=0

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Regular Issue Subscription Review Article
Volume 14
Issue 03
Received 17/07/2024
Accepted 02/10/2024
Published 18/11/2024
164