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Cancers
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Image Analysis and Machine Learning in Cancers: 2nd Edition
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Image Analysis and Machine Learning in Cancers: 2nd Edition

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 2678

Special Issue Editors

Dr. Helder C. R. De Oliveira

E-Mail Website
Guest Editor
Biometric Technologies Laboratory, Calgary, AB, Canada
Interests: medical imaging (mammography and digital breast tomosynthesis); machine learning; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Arianna Mencattini

E-Mail Website1 Website2
Guest Editor
Dipartimento di Ingegneria Elettronica, University of Rome Tor Vergata, Rome, Italy
Interests: image analysis; machine learning; medical applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the years, we have seen tremendous advances in image analysis and machine learning (ML) techniques for cancer detection. This phenomenon has been powered mainly by better equipment capturing higher-quality data, the availability of public datasets, and advances in computer technology that have enabled us to use methods that were once inaccessible. For example, we have seen many papers addressing super-resolution images, fusing images from different modalities to achieve better diagnosis, and even generating more data based on a limited available number.

Imaging processing techniques form the basis of all artificial intelligence (AI)-based systems. It has been proven that pre-processing methods have a substantial impact on the next steps in an ML/AI pipeline. Therefore, it is not surprising to see many papers proposing new methods to enhance images even further to achieve better results.

Machine learning approaches, also known as conventional approaches, have been essential in pushing boundaries in the detection and diagnosis of cancers. Since they can work with limited datasets and more modest computers, as opposed to deep learning approaches, many methods have been proposed since the popularization of AI. Nowadays, these methods can compete with DL-based ones in terms of accuracy, specificity, and sensitivity.

Deep learning (DL) techniques, developed based on the increased capabilities and accessibility of more powerful hardware, play an important role in this scenario. Since their basis lies in imaging analysis and machine learning, recent advances have shown many options for ensuring good diagnosis, reducing the length of follow-up in patients. However, DL models are known for their lack of explainability, although some studies have proposed ways to overcome this.

This Special Issue is dedicated to sharing the most recent advances in image analysis and machine learning techniques to achieve better detection/diagnosis in cancers. You are welcome to read the publications in the first edition at https://www.mdpi.com/journal/cancers/special_issues/WHG4FRJH4A.

Dr. Helder C. R. De Oliveira
Dr. Arianna Mencattini
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 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. Cancers 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 2900 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

  • cancer detection and diagnosis system
  • machine learning
  • deep learning
  • medical imaging analysis
  • few-shot deep learning
  • attention segmentation
  • feature extraction
  • probabilistic models
  • explainability
  • image fusion
  • generative adversarial network

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Published Papers (2 papers)

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Research

26 pages, 3973 KB  
Article
ViT-DCNN: Vision Transformer with Deformable CNN Model for Lung and Colon Cancer Detection
by Aditya Pal, Hari Mohan Rai, Joon Yoo, Sang-Ryong Lee and Yooheon Park
Cancers 2025, 17(18), 3005; https://doi.org/10.3390/cancers17183005 - 15 Sep 2025
Viewed by 695
Abstract
Background/Objectives: Lung and colon cancers remain among the most prevalent and fatal diseases worldwide, and their early detection is a serious challenge. The data used in this study was obtained from the Lung and Colon Cancer Histopathological Images Dataset, which comprises five different [...] Read more.
Background/Objectives: Lung and colon cancers remain among the most prevalent and fatal diseases worldwide, and their early detection is a serious challenge. The data used in this study was obtained from the Lung and Colon Cancer Histopathological Images Dataset, which comprises five different classes of image data, namely colon adenocarcinoma, colon normal, lung adenocarcinoma, lung normal, and lung squamous cell carcinoma, split into training (80%), validation (10%), and test (10%) subsets. In this study, we propose the ViT-DCNN (Vision Transformer with Deformable CNN) model, with the aim of improving cancer detection and classification using medical images. Methods: The combination of the ViT’s self-attention capabilities with deformable convolutions allows for improved feature extraction, while also enabling the model to learn both holistic contextual information as well as fine-grained localized spatial details. Results: On the test set, the model performed remarkably well, with an accuracy of 94.24%, an F1 score of 94.23%, recall of 94.24%, and precision of 94.37%, confirming its robustness in detecting cancerous tissues. Furthermore, our proposed ViT-DCNN model outperforms several state-of-the-art models, including ResNet-152, EfficientNet-B7, SwinTransformer, DenseNet-201, ConvNext, TransUNet, CNN-LSTM, MobileNetV3, and NASNet-A, across all major performance metrics. Conclusions: By using deep learning and advanced image analysis, this model enhances the efficiency of cancer detection, thus representing a valuable tool for radiologists and clinicians. This study demonstrates that the proposed ViT-DCNN model can reduce diagnostic inaccuracies and improve detection efficiency. Future work will focus on dataset enrichment and enhancing the model’s interpretability to evaluate its clinical applicability. This paper demonstrates the promise of artificial-intelligence-driven diagnostic models in transforming lung and colon cancer detection and improving patient diagnosis. Full article
(This article belongs to the Special Issue Image Analysis and Machine Learning in Cancers: 2nd Edition)
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10 pages, 2331 KB  
Article
Early-Stage Melanoma Benchmark Dataset
by Aleksandra Dzieniszewska, Piotr Garbat, Paweł Pietkiewicz and Ryszard Piramidowicz
Cancers 2025, 17(15), 2476; https://doi.org/10.3390/cancers17152476 - 26 Jul 2025
Viewed by 1693
Abstract
Background: The early detection of melanoma is crucial for improving patient outcomes, as survival rates decline dramatically with disease progression. Despite significant achievements in deep learning methods for skin lesion analysis, several challenges limit their effectiveness in clinical practice. One of the key [...] Read more.
Background: The early detection of melanoma is crucial for improving patient outcomes, as survival rates decline dramatically with disease progression. Despite significant achievements in deep learning methods for skin lesion analysis, several challenges limit their effectiveness in clinical practice. One of the key issues is the lack of knowledge about the melanoma stage distribution in the training data, raising concerns about the ability of these models to detect early-stage melanoma accurately. Additionally, publicly available datasets that include detailed information on melanoma stage and tumor thickness remain scarce, restricting researchers from developing and benchmarking methods specifically tailored for early diagnosis. Another major limitation is the lack of cross-dataset evaluations. Most deep learning models are tested on the same dataset they were trained on, so they fail to assess their generalization ability when applied to unseen data. This reduces their reliability in real-world clinical settings. Methods: We introduce an early-stage melanoma benchmark dataset to address these issues, featuring images labeled according to T-category based on Breslow thickness. Results: We evaluated several state-of-the-art deep learning models on this dataset and observed a significant drop in performance compared to their results on the ISIC Challenge datasets. Conclusions: This finding highlights the models’ limited capability in detecting early-stage melanoma. This work seeks to advance the development and clinical applicability of automated melanoma diagnostic systems by providing a resource for T-category-specific analysis and supporting cross-dataset evaluation. Full article
(This article belongs to the Special Issue Image Analysis and Machine Learning in Cancers: 2nd Edition)
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