Cancer Disease Identification and Recommendation Using Hybrid Deep Learning Algorithms

Authors

  • E Loganathan Assistant professor, Dept. of CSE, Erode Sengunthar Engg. college, Erode, Tamilnadu, India. Author
  • P Naveenkumar UG Scholar, Dept. of CSE, Erode Sengunthar Engg. college, Erode, Tamilnadu, India. Author
  • C Santhosh UG Scholar, Dept. of CSE, Erode Sengunthar Engg. college, Erode, Tamilnadu, India. Author
  • P Shankareshwaran UG Scholar, Dept. of CSE, Erode Sengunthar Engg. college, Erode, Tamilnadu, India. Author

DOI:

https://doi.org/10.47392/IRJASH.2025.006

Keywords:

Cancer identification, Image Processing, Convolutional Neural Network (CNN), Feature Extraction, Classification, Medical Recommendations

Abstract

It presents an automated system for cancer identification and classification using MATLAB, focusing on three types of cancer: brain tumour, skin cancer, and lung cancer. The system leverages advanced image processing techniques for cancer detection, feature extraction, and image segmentation to isolate cancerous regions in medical images. The core of the system is a Convolutional Neural Network (CNN), which is trained to predict the presence of cancer based on the extracted features. Feature selection methods are applied to reduce the complexity of the data, ensuring the CNN focuses on the most relevant characteristics of the suspected cancerous regions. The classification output not only confirms the presence of cancer but also distinguishes between different types of cancer, such as brain tumours, skin cancer, or lung cancer. Upon successful classification, the system provides medical recommendations, guiding clinicians toward appropriate next steps in diagnosis or treatment. This project aims to enhance cancer detection accuracy and efficiency, providing a non-invasive, automated solution to assist healthcare professionals in making informed decisions, potentially leading to earlier interventions and better patient care outcomes.

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Published

2025-01-31