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The Imaging Science Journal Volume 67, 2019 - Issue 8
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Research Articles

Infection level identification for leukemia detection using optimized Support Vector Neural Network

Biplab Kanti DasCalcutta Institute of Technology, Uluberia, IndiaCorrespondence[email protected]
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Himadri Sekhar DuttaKalyani Government Engineering College, Kalyani, IndiaView further author information
Pages 417-433 | Received 27 Dec 2018, Accepted 20 Nov 2019, Published online: 27 Dec 2019

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Pranshu Saxena & Anjali Goyal. (2022) Computer-assisted grading of follicular lymphoma: a classification based on SVM, machine learning, and transfer learning approaches. The Imaging Science Journal 70:1, pages 30-45.
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Articles from other publishers (5)

Caiwen Xu, Jie Feng, Yong Yue, Wanjun Cheng, Dianning He, Shouliang Qi & Guojun Zhang. (2024) A hybrid few-shot multiple-instance learning model predicting the aggressiveness of lymphoma in PET/CT images. Computer Methods and Programs in Biomedicine 243, pages 107872.
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Chepati Dhana LakshmiMissMissGajjala Satish Kumar ReddyMrMrChukka Yaswanth KumarMrMrChinta MounikaMissMissT Ravi SekharMrMr. (2023) Weed Identification in Agricultural Fields Using Machine Learning Techniques. Electrical and Automation Engineering 2:1, pages 97-103.
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Reem Magdy Elrefaie, Elsaid A.Marzouk, Mohamed A.Mohamed & Mohamed Maher Ata. (2022) Supervised Acute Lymphocytic Leukemia Detection and Classification Based - Empirical Mode Decomposition. Supervised Acute Lymphocytic Leukemia Detection and Classification Based - Empirical Mode Decomposition.
Indrajeet Kumar, Chandradeep Bhatt, Vrince Vimal & Shamimul Qamar. (2022) Automated white corpuscles nucleus segmentation using deep neural network from microscopic blood smear. Journal of Intelligent & Fuzzy Systems 42:2, pages 1075-1088.
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Mohammad Akter Hossain, A. K. M. Muzahidul Islam, Salekul Islam, Swakkhar Shatabda & Ashir Ahmed. (2022) Symptom Based Explainable Artificial Intelligence Model for Leukemia Detection. IEEE Access 10, pages 57283-57298.
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