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Applied Sciences
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Object Detection and Image Processing Based on Computer Vision
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Object Detection and Image Processing Based on Computer Vision

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 March 2026 | Viewed by 1045

Special Issue Editor

Dr. Victor H. Diaz-Ramirez

E-Mail Website
Guest Editor
Instituto Politécnico Nacional—CITEDI, Ave. Instituto Politécnico Nacional 1310, Tijuana 22310, Mexico
Interests: image processing; computer vision; pattern recognition; three-dimensional reconstruction; multiocular vision

Special Issue Information

Dear Colleagues,

Object detection and image processing are essential tasks in modern computer vision systems. Reliable methods for these tasks enable the solution of diverse computer vision problems, including autonomous driving, medical imaging, facial recognition, manufacturing, and environmental monitoring, among others.

Research in object detection and image processing based on computer vision has grown exponentially over the past decades, resulting from improvements in imaging sensor quality and resolution, significant increases in the availability of computational resources, and advancements in machine learning methods. Additionally, the broad applicability of these tasks makes them inherently interdisciplinary.

The objective of this Special Issue is to invite original state-of-the-art research contributions addressing broad challenges in object detection and image processing based on computer vision. Additionally, it aims to create a multidisciplinary forum to discuss recent advances in computer vision and explore new applications across scientific fields, including science, engineering, economics, and social activities.

We invite articles from all areas of computer vision that explore the synergy between mathematical models, object detection, image processing, image classification, tracking, video understanding, and deep learning techniques. We believe this Special Issue will provide a timely collection of research updates, benefiting researchers and practitioners across the diverse computer vision community.

Dr. Victor H. Diaz-Ramirez
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 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. Applied Sciences 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 2400 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

  • computer vision
  • image processing
  • object detection
  • image classification
  • target tracking
  • medical imaging
  • 3D imaging
  • deep learning methods

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Published Papers (1 paper)

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Research

24 pages, 8171 KB  
Article
Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
by Edgar Omar Molina Molina and Victor H. Diaz-Ramirez
Appl. Sci. 2025, 15(14), 7879; https://doi.org/10.3390/app15147879 - 15 Jul 2025
Viewed by 646
Abstract
Breast cancer is a leading cause of mortality among women worldwide. Early detection is crucial for increasing patient survival rates. Artificial intelligence, particularly convolutional neural networks (CNNs), has enabled the development of effective diagnostic systems by digitally processing mammograms. CNNs have been widely [...] Read more.
Breast cancer is a leading cause of mortality among women worldwide. Early detection is crucial for increasing patient survival rates. Artificial intelligence, particularly convolutional neural networks (CNNs), has enabled the development of effective diagnostic systems by digitally processing mammograms. CNNs have been widely used for the classification of breast cancer in images, obtaining accurate results similar in many cases to those of medical specialists. This work presents a hybrid feature extraction approach for breast cancer detection that employs variants of EfficientNetV2 network and convenient image representation based on phase features. First, a region of interest (ROI) is extracted from the mammogram. Next, a three-channel image is created using the local phase, amplitude, and orientation features of the ROI. A feature vector is constructed for the processed mammogram using the developed CNN model. The size of the feature vector is reduced using simple statistics, achieving a redundancy suppression of 99.65%. The reduced feature vector is classified as either malignant or benign using a classifier ensemble. Experimental results using a training/testing ratio of 70/30 on 15,506 mammography images from three datasets produced an accuracy of 86.28%, a precision of 78.75%, a recall of 86.14%, and an F1-score of 80.09% with the modified EfficientNetV2 model and stacking classifier. However, an accuracy of 93.47%, a precision of 87.61%, a recall of 93.19%, and an F1-score of 90.32% were obtained using only CSAW-M dataset images. Full article
(This article belongs to the Special Issue Object Detection and Image Processing Based on Computer Vision)
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