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Article

Improved Dominance Soft Set Based Decision Rules with Pruning for Leukemia Image Classification

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Department of Information Technology, Sona College of Technology (Autonomous), Salem, Tamil Nadu 636005, India
2
Department of Computer Science, Periyar University, Salem, Tamil Nadu 636 011, India
3
Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh 11586, Saudi Arabia
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Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, Egypt
5
Faculty of Engineering, Cairo University, Giza 12613, Egypt
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(5), 794; https://doi.org/10.3390/electronics9050794
Received: 24 February 2020 / Revised: 12 April 2020 / Accepted: 1 May 2020 / Published: 12 May 2020
(This article belongs to the Special Issue Machine Learning Applied to Medical Image Analysis)
Acute lymphoblastic leukemia is a well-known type of pediatric cancer that affects the blood and bone marrow. If left untreated, it ends in fatal conditions due to its proliferation into the circulation system and other indispensable organs. All over the world, leukemia primarily attacks youngsters and grown-ups. The early diagnosis of leukemia is essential for the recovery of patients, particularly in the case of children. Computational tools for medical image analysis, therefore, have significant use and become the focus of research in medical image processing. The particle swarm optimization algorithm (PSO) is employed to segment the nucleus in the leukemia image. The texture, shape, and color features are extracted from the nucleus. In this article, an improved dominance soft set-based decision rules with pruning (IDSSDRP) algorithm is proposed to predict the blast and non-blast cells of leukemia. This approach proceeds with three distinct phases: (i) improved dominance soft set-based attribute reduction using AND operation in multi-soft set theory, (ii) generation of decision rules using dominance soft set, and (iii) rule pruning. The efficiency of the proposed system is compared with other benchmark classification algorithms. The research outcomes demonstrate that the derived rules efficiently classify cancer and non-cancer cells. Classification metrics are applied along with receiver operating characteristic (ROC) curve analysis to evaluate the efficiency of the proposed framework. View Full-Text
Keywords: leukemia; soft set theory; decision rules; feature extraction; feature selection; particle swarm optimization algorithm leukemia; soft set theory; decision rules; feature extraction; feature selection; particle swarm optimization algorithm
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MDPI and ACS Style

Jothi, G.; Inbarani, H.H.; Azar, A.T.; Koubaa, A.; Kamal, N.A.; Fouad, K.M. Improved Dominance Soft Set Based Decision Rules with Pruning for Leukemia Image Classification. Electronics 2020, 9, 794. https://doi.org/10.3390/electronics9050794

AMA Style

Jothi G, Inbarani HH, Azar AT, Koubaa A, Kamal NA, Fouad KM. Improved Dominance Soft Set Based Decision Rules with Pruning for Leukemia Image Classification. Electronics. 2020; 9(5):794. https://doi.org/10.3390/electronics9050794

Chicago/Turabian Style

Jothi, Ganesan, Hannah H. Inbarani, Ahmad Taher Azar, Anis Koubaa, Nashwa Ahmad Kamal, and Khaled M. Fouad. 2020. "Improved Dominance Soft Set Based Decision Rules with Pruning for Leukemia Image Classification" Electronics 9, no. 5: 794. https://doi.org/10.3390/electronics9050794

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