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Paper ID:IJSREM62205

Machine Learning and Deep Learning Approaches for Medical Image Analysis and Disease Detection Using X-ray and MRI Harsh Kumar Sahu

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62205

Volume:

10 |

4 |

April 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM62205

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

HKs

Harsh Kumar sahu

Amity Institute of information Technology

Amity university Raipur Chhattisgarh

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ST

Shikha Tiwari

Amity Institute of information Technology

Amity university Raipur Chhattisgarh

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AP

Aryan Pandey

Amity Institute of information Technology

Amity university Raipur Chhattisgarh

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AM

Atul Mishra

Amity Institute of information Technology

Amity university Raipur Chhattisgarh

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Rk

Rohit kurrey

Amity Institute of information Technology

Amity university Raipur Chhattisgarh

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GV

Gulshan Verma

Amity Institute of information Technology

Amity university Raipur Chhattisgarh

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Abstract

Today, medical image analysis has gained significant importance as it allows for accurate diagnosis of various illnesses at an early stage. In the present research, I propose the use of computer vision and deep learning methods for automatic detection of abnormalities based on X-ray and Magnetic Resonance Imaging (MRI) results. The proposed approach will be based on using convolutional neural networks (CNNs) for identifying relevant features in the analyzed medical images and their classification as abnormal or normal. In the research process, such steps as noise filtering, image normalization, and contrast optimization will be taken. Then, a deep learning algorithm will be used for recognizing abnormalities in images, including those of lungs, brain, and bones with the help of X-ray and MRI. In addition, transfer learning will be used to achieve higher precision by making use of pre-trained CNNs such as ResNet and VGG. It was evident from the results of experiments that the proposed method is extremely accurate, sensitive, specific and it can greatly help the medical experts. The proposed method will certainly aid in avoiding diagnostic mistakes, save time and contribute to improving patient’s health, particularly for patients living in areas where expert radiologists are scarce. The current study once again highlights the necessity of using artificial intelligence along with imaging techniques in designing diagnostic devices in future.

KEYWORDS

Keywords:- Abnormality Detection Artificial Intelligence in Healthcare Computer Vision Convolutional Neural Networks Deep Learning Image Classification Magnetic Resonance Imaging Medical Image Analysis Transfer Learning X-ray Imaging.

Cite This Article

Harsh Kumar sahu, Shikha Tiwari, Aryan Pandey, Atul Mishra, Rohit kurrey, Gulshan Verma, "Machine Learning and Deep Learning Approaches for Medical Image Analysis and Disease Detection Using X-ray and MRI Harsh Kumar Sahu," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-6, April 2026. https://doi.org/10.55041/IJSREM62205

Full Article/Conclusion

To begin with, it gives me great pleasure to thank my project guide and faculties who gave me their valuable guidance and support in conducting my research on “Medical Image Analysis for Disease Detection using X-Ray and MRI.”   Secondly, I owe a lot to my school which has always been supportive in providing me with all the resources that I required during my research work. Furthermore, I am grateful to all my friends and fellow researchers who came forward in helping me in doing my project.   Lastly, but by no means the least, I am thankful to my family members for always motivating and encouraging me.

References

[1] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.   [2] G. Litjens et al., “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, 2017.   [3] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Proc. MICCAI, 2015, pp. 234–241.   [4] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. CVPR, 2016, pp. 770–778.   [5] D. Shen, G. Wu, and H.-I. Suk, “Deep learning in medical image analysis,” Annual Review of Biomedical Engineering, vol. 19, pp. 221–248, 2017.   [6] S. Rajpurkar et al., “CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning,” 2017.   [7] M. Anthimopoulos et al., “Lung pattern classification for interstitial lung diseases using a deep convolutional neural network,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1207–1216, 2016.   [8] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proc. CVPR, 2015.   [9] A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. NIPS, 2012.   [10] T. Doherty et al., “Applications of artificial intelligence in medical imaging,” Journal of Medical Systems, vol. 42, no. 11, 2018.  

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Publication Date

5/2/2026

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