Efforts of Neutrosophic Logic in Medical Image Processing and Analysis
Azmi Shawkat Abdulbaqi1, Bourair Al-Attar2, Lateef Abd Zaid Qudr3, Harshavardhan Reddy Penubadi4,5, Ravi Sekhar4,*, Pritesh Shah4, Sushma Parihar4, Sushmitha Kallam6, Jamal Fadhil Tawfeq7, Hassan muwafaq Gheni 8
1University of Anbar, Renewable Energy Research Center, Ramadi, Iraq
2College of Medicin University of Al-Ameed Karbala PO Box 198, Iraq
3Department of Computer, Techniques Engineering, AlSafwa University College, Almamalje str.,
56001, Karbala, Iraq
4Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University) (SIU), Pune 412115, Maharashtra, India
5Myriad Genetics, Salt Lake City, UT, USA
6Rajiv Gandhi University of Health Sciences, Bengaluru 560041, Karnataka, India
7Department of Medical Instrumentation Technical Engineering, Medical Technical College, Al-Farahidi University, Baghdad 00965, Iraq
8Computer Techniques Engineering Department, Al-Mustaqbal University College, Hillah 51001, Iraq
Emails:azmi_msc@uoanbar.edu.iq; bourair.alattar@alameed.edu.iq;latifkhder@alsafwa.edu.iq;harshavdevops99@gmail.com , ravi.sekhar@sitpune.edu.in; pritesh.shah@sitpune.edu.in; sushmap@sitpune.edu.in,dr.sushmithareddy18@gmail.com; jamaltawfeq55@gmail.com; hasan.muwafaq@mustaqbal-college.edu.iq
Abstract
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.
Keywords: Neutrosophic Logic; Medical Image Processing; Noise Reduction; Image Enhancement; Image Segmentation; Feature Extraction; Image Classification; Uncertainty Handling