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Biomedicines
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Artificial Intelligence Applications in Cancer and Other Diseases—2nd Edition
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Artificial Intelligence Applications in Cancer and Other Diseases—2nd Edition

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Biomedical Engineering and Materials".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1724

Special Issue Editor

Prof. Dr. Raji Sundararajan

E-Mail Website
Guest Editor
School of Engineering Technology, Purdue University, Knoy Hall of Technology, West Lafayette, IN 47907, USA
Interests: artificial intelligence; machine learning; neural networks; deep learning; obesity; diabetes; cancer; other diseases; pathology; drug discovery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and its subsets, machine learning, neural networks, deep learning, etc., have the potential to revolutionize the medical field. AI is not only useful for analyzing medical images, such as ECG, EEG, etc., but is also useful for labelled and unlabeled data. Various machine learning algorithms, such as naïve basis, support vector machines (SVMs), etc., are useful in predicting breast cancer occurrence, pattern, and early detection. AI can be used for both communicable and non-communicable diseases. The supervised learning, unsupervised learning, and semi-supervised learning models of machine learning have advanced algorithms to work on the type of data available in addition to the images most commonly used in this kind of research. With the possibility of one out of two men and one out of three women suffering from cancer in the US, as well as the global increase in obesity, diabetes, cancer, and other diseases, the need for additional tools, besides conventional ones, such as AI in the early detection and prediction of cancer and other diseases, as well as its applications in pathology, drug discovery, etc., cannot be overstated. Towards this end, this Special Issue invites original research articles as well as detailed review articles and short communications on the applications of AI in cancer and other diseases.

Prof. Dr. Raji Sundararajan
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Biomedicines is an international peer-reviewed open access monthly 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

  • artificial intelligence
  • machine learning
  • neural networks
  • deep learning
  • obesity
  • diabetes
  • cancer
  • other diseases
  • pathology
  • drug discovery

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Published Papers (2 papers)

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Research

30 pages, 5126 KB  
Article
CT-Malaria Detection via Adaptive-Weighted Deep Learning Models
by Karim Gasmi, Moez Krichen, Afrah Alanazi, Sahar Almenwer, Sarah Almaghrabi and Samia Yahyaoui
Biomedicines 2026, 14(4), 898; https://doi.org/10.3390/biomedicines14040898 - 15 Apr 2026
Viewed by 386
Abstract
Context: In numerous low- and middle-income nations, malaria remains a significant issue due to the challenges associated with diagnosing it through thin blood smears. The appearance of images can vary significantly depending on the microscope type, magnification, lighting conditions, slide preparation methods, and [...] Read more.
Context: In numerous low- and middle-income nations, malaria remains a significant issue due to the challenges associated with diagnosing it through thin blood smears. The appearance of images can vary significantly depending on the microscope type, magnification, lighting conditions, slide preparation methods, and staining techniques. Due to the delicate morphology of parasites, false negatives might adversely affect patient care. Objective: To achieve optimal outcomes from validation, it is essential to construct a robust and easily replicable process. This pipeline should integrate the optimal elements of classical machine learning and end-to-end deep learning, enhance reliability by pairwise ensembling, and select ensemble weights in a logical, data-driven manner. Method: To achieve our objective, we propose two tracks. The initial track encompasses real-time augmentation, convolution-based feature extraction, and the training of calibrated classical classifiers. The second module focuses on training many convolutional networks from inception to completion. Subsequently, we construct paired ensembles and employ a hybrid methodology to select convex weights for combining the findings. This method initially evaluates a set of candidate weights and then refines them to maximise validation accuracy. Results: The precision of the two-track architecture consistently improves, transitioning from conventional baselines to end-to-end models. Optimal and consistent enhancements are achieved through weighted ensembling. Utilising optimised fusion reduces the incidence of false negatives for subtle parasites and false positives caused by staining artefacts. This yields an accuracy of 96.35% on the reserved data and reduced variance across folds. Conclusions: The integration of augmentation, multiple modelling tracks, and optimal pairwise ensembling yields the highest accuracy in categorising malaria smears. It facilitates further enhancements by incorporating supplementary models, multi-class extensions, and operating-point calibration. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Cancer and Other Diseases—2nd Edition)
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14 pages, 2065 KB  
Article
Automated Early Detection of Skin Cancer Using a CNN-ViT-Attention-Based Hybrid Model
by Zekiye Kanat, Merve Kesim Onal, Harun Bingol, Serpil Sener, Engin Avci and Muhammed Yildirim
Biomedicines 2026, 14(3), 583; https://doi.org/10.3390/biomedicines14030583 - 5 Mar 2026
Cited by 1 | Viewed by 829
Abstract
Background/Objectives: Skin cancer is a very serious disease. There is a risk that the cancer will spread to other parts of the body as the cancerous tissue deepens. For this reason, early diagnosis is important because it allows for early initiation of [...] Read more.
Background/Objectives: Skin cancer is a very serious disease. There is a risk that the cancer will spread to other parts of the body as the cancerous tissue deepens. For this reason, early diagnosis is important because it allows for early initiation of treatment. This study proposes a hybrid model for the early diagnosis of skin cancer. Methods: The proposed model was developed using Convolutional Neural Networks (CNNs), Vision Transformer (ViT) architectures, and the k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Neural Network Classifiers, Decision Tree (DT), and Logistic Regression (LR) classifiers. Furthermore, the proposed model was fine-tuned to improve its disease diagnosis. Two attention mechanisms, channel and spatial, were used together in the proposed model. The HAM10000 dataset was used during the experiments. Class weighting was performed to ensure class-based balance in the dataset. Results: The proposed model was also compared with the CNN and ViT architectures frequently used in the literature. Among these models, the highest accuracy value of 95.1% was obtained with the proposed model. Conclusions: It is considered that the proposed model can be used as a decision support system for dermatologists in the diagnosis of skin cancer. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Cancer and Other Diseases—2nd Edition)
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