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Digital Image Processing and Deep Learning in the Fields of Medical Image Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 1454

Special Issue Editor


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Guest Editor
Department of Energy, Electricity and Automatic, HEI, 13 rue de Toul, 59046 Lille, France
Interests: neuroimaging; bioinformatics and computational biology; signal, image and video processing; digital image processing; pattern recognition

Special Issue Information

Dear Colleagues,

Nowadays, deep learning methods have become a standard approach for image processing.

A close look at the literature shows that the core of the system remains the same; the emphasis is generally on two points, the pre-processing of the data and the constitution of the database used to drive the system. In the fields of medical image processing, database constitution necessitates a co-working approach between clinicians and engineers to ensure an efficient ‘learning’ stage with a correct explainability of results.

Questions to answer are: How can we guide clinicians in the constitution of adapted databases? That means how to prepare data to optimize/ to drive the results. How do we assess the results of DL algorithms? How to compare the ground truth with the results of the algorithms (precisions, errors...).

Dr. Laurent Peyrodie
Guest Editor

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Keywords

  • medical image processing
  • deep learning

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Published Papers (1 paper)

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Research

21 pages, 5797 KiB  
Article
Identification of Lacerations Caused by Cervical Cancer through a Comparative Study among Texture-Extraction Techniques
by Jorge Aguilar-Santiago, José Trinidad Guillen-Bonilla, Mario Alberto García-Ramírez and Maricela Jiménez-Rodríguez
Appl. Sci. 2023, 13(14), 8292; https://doi.org/10.3390/app13148292 - 18 Jul 2023
Cited by 1 | Viewed by 1133
Abstract
Cervical cancer is a disease affecting a worrisomely large number of women worldwide. If not treated in a timely fashion, this disease can lead to death. Due to this problematic, this research employed the LBP, OC_LBP, CS-LTP, ICS-TS, and CCR texture descriptors for [...] Read more.
Cervical cancer is a disease affecting a worrisomely large number of women worldwide. If not treated in a timely fashion, this disease can lead to death. Due to this problematic, this research employed the LBP, OC_LBP, CS-LTP, ICS-TS, and CCR texture descriptors for the characteristic extractions of 60 selected carcinogenic images classified as Types 1, 2, and 3 according to a database; afterward, a statistical multi-class classifier and an NN were used for image classification. The resulting characteristic vectors of all five descriptors were implemented in four tests to identify the images by type. The statistical multi-class combination and classification of all images achieved a classification efficiency of 83–100%. On the other hand, with the NN, the LBP, OC_LBP, and CCR descriptors presented a classification efficiency of between 81.6 and 98.3%, differing from that of ICS_TS and CS_LTP, which ranged from 36.6 to 55%. Based on the tests performed with regard to ablation, ROC curves, and confusion matrix, we consider that an efficient expert system can be developed with the objective of detecting cervical cancer at early stages. Full article
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