Volume 24 , Issue 4 , PP: 376-388, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Azmi Shawkat Abdulbaqi 1 , Bourair Al-Attar 2 , Lateef Abd Zaid Qudr 3 , Harshavardhan Reddy Penubadi 4 , Ravi Sekhar 5 * , Pritesh Shah 6 , Sushma Parihar 7 , Sushmitha Kallam 8 , Jamal Fadhil Tawfeq 9 , Hassan muwafaq Gheni 10
Doi: https://doi.org/10.54216/IJNS.240428
Medical image processing is indispensable for correct diagnosis and planning of treatment. However, it is susceptible to many errors due to noise, artifacts, and the variability innate in anatomical structures themselves. Traditional image analysis methods hence suffer from these complexities in the images themselves and lead to probable inaccuracies in image analysis. This paper probes into the role of neutrosophic logic in the domain of medical image processing to seek better handling of these problems. The main objectives of the work were to optimize the noise reduction, image segmentation, feature extraction, and classification using the special capabilities of neutrosophic logic directed toward handling uncertainty and indeterminacy. Contributions The contributions of this study are multifaceted: it contributes by introducing detailed support for applying neutrosophic logic in a number of medical image processing tasks and integrates neutrosophic logic with prior techniques and evaluates their performance with traditional methods. The experimental results in the study are complete and demonstrate significant improvements in key metrics. For example, applying neutrosophic logic in noise reduction increased the peak signal-to-noise ratio of MRI images from 25 dB to 35 dB. In some segmentation tasks, the Dice coefficient for liver CT scans increased from 0.85 to 0.92. It increases the accuracy of feature extraction in breast cancer detection from 88% to 95%, while integrating neutrosophic logic with convolutional neural networks improves the accuracy in retinal image classification from 92% to 97%. All these results underline the strong role that neutrosophic logic can play in enhancing accuracy, robustness, and reliability in the processing of medical images. The result of the study concludes that neutrosophic logic not only improves the current limitations but also holds great promise for handling uncertainty in many medical fields, opening a promising way for future advancements in the field of medical imaging and health applications.
Neutrosophic Logic , Medical Image Processing , Noise Reduction , Image Enhancement , Image , , Segmentation , Feature Extraction , Image Classification , , Uncertainty Handling
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