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Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques (2nd Edition)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 20 February 2026 | Viewed by 1075

Special Issue Editor


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Guest Editor
Medical Physics, Radiobiology and Radiological Protection Group, Research Center of the Portuguese Institute of Oncology of Porto (CI-IPOP), 4200-072 Porto, Portugal
Interests: pattern recognition; image processing; biomedical applications; data science; artificial intelligence; machine learning; deep learning
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Special Issue Information

Dear Colleagues,

Medical imaging has become an essential component in many fields of medical research and clinical practice. Medical imaging techniques, deep learning, and artificial intelligence bring many healthcare protection benefits. We can now collect, measure, and analyse vast volumes of health-related data using computing, networking technologies, and artificial intelligence, leading to tremendous advances in healthcare and excellent opportunities for medical imaging communities. Meanwhile, these technologies have also brought new challenges and issues.

This Special Issue of the Journal Sensors is focused on advanced techniques, new challenges, and issues in Medical imaging.

Dr. Ines Domingues
Guest Editor

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Keywords

  • medical imaging 
  • artificial intelligence 
  • information fusion for medical data 
  • image recognition 
  • machine learning 
  • deep learning 
  • image processing 
  • image analysis 
  • computer vision

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Published Papers (1 paper)

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Research

28 pages, 2869 KB  
Article
Enhancing Medical Image Segmentation and Classification Using a Fuzzy-Driven Method
by Akmal Abduvaitov, Abror Shavkatovich Buriboev, Djamshid Sultanov, Shavkat Buriboev, Ozod Yusupov, Kilichov Jasur and Andrew Jaeyong Choi
Sensors 2025, 25(18), 5931; https://doi.org/10.3390/s25185931 - 22 Sep 2025
Viewed by 714
Abstract
Automated analysis for tumor segmentation and illness classification is hampered by the noise, low contrast, and ambiguity that are common in medical pictures. This work introduces a new 12-step fuzzy-based improvement pipeline that uses fuzzy entropy, fuzzy standard deviation, and histogram spread functions [...] Read more.
Automated analysis for tumor segmentation and illness classification is hampered by the noise, low contrast, and ambiguity that are common in medical pictures. This work introduces a new 12-step fuzzy-based improvement pipeline that uses fuzzy entropy, fuzzy standard deviation, and histogram spread functions to enhance picture quality in CT, MRI, and X-ray modalities. The pipeline produces three improved versions per dataset, lowering BRISQUE scores from 28.8 to 21.7 (KiTS19), 30.3 to 23.4 (BraTS2020), and 26.8 to 22.1 (Chest X-ray). It is tested on KiTS19 (CT) for kidney tumor segmentation, BraTS2020 (MRI) for brain tumor segmentation, and Chest X-ray Pneumonia for classification. A Concatenated CNN (CCNN) uses the improved datasets to achieve a Dice coefficient of 99.60% (KiTS19, +2.40% over baseline), segmentation accuracy of 0.983 (KiTS19) and 0.981 (BraTS2020) versus 0.959 and 0.943 (CLAHE), and classification accuracy of 0.974 (Chest X-ray) versus 0.917 (CLAHE). A classic CNN is trained on original and CLAHE-filtered datasets. These outcomes demonstrate how well the pipeline works to improve image quality and increase segmentation/classification accuracy, offering a foundation for clinical diagnostics that is both scalable and interpretable. Full article
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