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A hybrid machine learning approach for automated malaria diagnosis from thin blood smear images.

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第一作者： Ghufran Alam,Siddiqui

第一单位： Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, Pilani Campus, Vidya Vihar, Pilani, Rajasthan, 333031, India.

作者： Ghufran Alam,Siddiqui ^[1] ; Asish,Bera ^[1] ; Ankur,Lhila ^[2] ; Amitesh Singh,Rajput ^[3] ; Nitika,Nitika ^[4] ; Praveen K,Bharti ^[4] ; Ashis,Das ^[5] ; Tanmaya,Mahapatra ^[6]

作者单位： Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, Pilani Campus, Vidya Vihar, Pilani, Rajasthan, 333031, India. ^[1] Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani, Pilani Campus, Vidya Vihar, Pilani, Rajasthan, 333031, India. ^[2] Rajiv Gandhi National Cyber Law Centre, National Law Institute University, Kerwa Dam Road, Bhopal, Madhya Pradesh, 462044, India. ^[3] National Institute of Malaria Research, Indian Council of Medical Research, Sector 8, Dwarka, New Delhi, Delhi, 110077, India. ^[4] Molecular Parasitology and Systems Biology Laboratory, Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Pilani Campus, Vidya Vihar, Pilani, Rajasthan, 333031, India. ^[5] Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, Pilani Campus, Vidya Vihar, Pilani, Rajasthan, 333031, India. tanmaya.mahapatra@pilani.bits-pilani.ac.in. ^[6]

关键词 Automated diagnosis Convolutional neural network Hybrid deep learning Malaria diagnosis Microscopy Otsu thresholding

DOI 10.1186/s13071-026-07438-6

PMID 42143374

发布时间 2026-05-16

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