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Mathematics
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Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms...
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Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms 2023

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 1259

Special Issue Editors

Dr. Cornelio Yáñez Márquez

E-Mail Website
Guest Editor
Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, Mexico
Interests: deep learning; associative models; machine learning; pattern recognition; neural networks; metaheuristics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yenny Villuendas-Rey

E-Mail Website
Guest Editor
Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Ciudad de México, México
Interests: optimization; bio-inspired algorithms; machine learning; rough sets; biomedical applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The arrival of the third millennium has brought impressive developments and advances in machine learning algorithms. Recent advances in deep learning, the algorithms of which are accelerated with CUDA hardware cards, deserve special mention. The applications of this type of algorithm have permeated a wide range of human activities, including the sensitive area of health research.

The contents of high-impact research journals bear witness to the efforts of scientists on such relevant topics as the classification, diagnosis and prognosis of diseases. Given the speed with which these investigations are advancing, due to the rapid development of new hardware, software and application platforms, it is necessary to promote new investigations that support physicians and health researchers.

This Special Issue seeks unpublished contributions of high scientific quality on the topic of the classification, diagnosis and prognosis of diseases using machine learning algorithms.

Dr. Cornelio Yáñez Márquez
Prof. Dr. Yenny Villuendas-Rey
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • classification of diseases
  • diagnosis of diseases
  • prognosis of diseases
  • cancer
  • chronic diseases
  • artificial intelligence
  • associative memories
  • deep learning
  • data mining
  • big data

Published Papers (1 paper)

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Research

26 pages, 4142 KiB  
Article
Breast Cancer Detection and Classification Using Hybrid Feature Selection and DenseXtNet Approach
by Mohammed Alshehri
Mathematics 2023, 11(23), 4725; https://doi.org/10.3390/math11234725 - 22 Nov 2023
Viewed by 908
Abstract
Breast Cancer (BC) detection and classification are critical tasks in medical diagnostics. The lives of patients can be greatly enhanced by the precise and early detection of BC. This study suggests a novel approach for detecting BC that combines deep learning models and [...] Read more.
Breast Cancer (BC) detection and classification are critical tasks in medical diagnostics. The lives of patients can be greatly enhanced by the precise and early detection of BC. This study suggests a novel approach for detecting BC that combines deep learning models and sophisticated image processing techniques to address those shortcomings. The BC dataset was pre-processed using histogram equalization and adaptive filtering. Data augmentation was performed using cycle-consistent GANs (CycleGANs). Handcrafted features like Haralick features, Gabor filters, contour-based features, and morphological features were extracted, along with features from deep learning architecture VGG16. Then, we employed a hybrid optimization model, combining the Sparrow Search Algorithm (SSA) and Red Deer Algorithm (RDA), called Hybrid Red Deer with Sparrow optimization (HRDSO), to select the most informative subset of features. For detecting BC, we proposed a new DenseXtNet architecture by combining DenseNet and optimized ResNeXt, which is optimized using the hybrid optimization model HRDSO. The proposed model was evaluated using various performance metrics and compared with existing methods, demonstrating that its accuracy is 97.58% in BC detection. MATLAB was utilized for implementation and evaluation purposes. Full article
(This article belongs to the Special Issue Classification, Diagnosis and Prognosis of Diseases Using Machine Learning Algorithms 2023)
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