Enhancing Medical Image Analysis Through Deep Learning-Based Lesion Detection

Authors

  • Anant Nagesh Kaulage Assistant Professor, Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India
  • Raju M. Sairise Principal, Yadavrao Tasgaonkar college of Engineering and Management, Karjat, Raigad, Pune, Maharashtra, India
  • Niket Pundlikrao Tajne Symbiosis School for Online and Digital Learning, Pune, Maharashtra, India
  • Pallawi Unmesh Bulakh Assistant Professor, Department of Computer Science, Modern College of Arts, Science and Commerce, Shivaji Nagar, Pune, Maharashtra, India
  • Sarika T. Deokate Assistant Professor, Department of Computer Engineering, Pimpri Chinchwad College of Engineering Nigdi, Pune, Maharashtra, India
  • Satpalsing D. Rajput Assistant Professor, Department of Computer Engineering, Pimpri Chinchwad College of Engineering Nigdi, Pune, Maharashtra, India

Keywords:

Lesion Detection, Medical Imaging, Deep Learning, Genetic Algorithm, Healthcare

Abstract

Medical image analysis plays a pivotal role in modern healthcare, aiding clinicians in the timely and accurate diagnosis of various ailments. Lesion detection, in particular, is a critical component of this process, where the need for improved efficiency and accuracy remains a pressing concern. This paper presents a novel approach that leverages the power of deep learning and genetic algorithms to address these challenges and enhance lesion detection in medical images.

Existing lesion detection methods often struggle with two major limitations: the demand for extensive labeled data and the ability to capture intricate lesion boundaries. This work aims to overcome these challenges by proposing a Unified Neural Network (UNet) architecture, a popular choice in medical image analysis, coupled with a Genetic Algorithm (GA) optimization technique. This synergistic combination facilitates significant improvements in both efficiency and accuracy.

Our proposed method begins by training a UNet model on a limited dataset of annotated medical images, reducing the need for extensive manual labeling. To address the issue of precise boundary delineation, the Genetic Algorithm is employed to fine-tune the model, optimizing its parameters for lesion detection. This dynamic approach empowers the model to adapt and learn from the data, enhancing its ability to identify lesions with higher precision. The advantages of our approach are manifold. Firstly, it substantially reduces the labeling burden on medical experts, making it more feasible to scale up lesion detection efforts across diverse medical domains. Secondly, the integration of the Genetic Algorithm ensures that the UNet model reaches optimal performance, resulting in more accurate and reliable lesion detection. Additionally, our method exhibits robustness across different imaging modalities, making it adaptable for a wide range of medical image analysis tasks.

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Published

27.10.2023

How to Cite

Kaulage, A. N. ., Sairise, R. M. ., Tajne, N. P. ., Bulakh, P. U. ., Deokate, S. T. ., & Rajput, S. D. . (2023). Enhancing Medical Image Analysis Through Deep Learning-Based Lesion Detection. International Journal of Intelligent Systems and Applications in Engineering, 12(2s), 27–36. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/3556

Issue

Vol. 12 No. 2s (2024)

Section

Research Article

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