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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 19657

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Special Issue Editors

Dr. Cecilia Di Ruberto

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Guest Editor

Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy
Interests: computer vision; image retrieval; biomedical image analysis; pattern recognition and machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Andrea Loddo

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Guest Editor

Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy
Interests: computer vision; image processing; machine learning; deep learning; artificial intelligence; medical image analysis; biomedical image analysis
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Dr. Lorenzo Putzu

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Guest Editor

Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123 Cagliari, Italy
Interests: computer vision; medical image analysis; shape analysis and matching; image retrieval and classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of the journal Applied Sciences, entitled “Computer Aided Diagnosis”, aims to present recent advances in the generation and utilization of features and machine learning techniques for biomedical image classification and retrieval. The recent advances of machine learning techniques, mostly based on deep learning, have significantly influenced the design and performance of computer aided diagnosis systems. Nowadays, deep features are often preferred to hand-crafted ones, but, at the same time, their complexity, and the poor interpretability of the extracted data, have not favoured its wide use in real applications. This Special Issue places particular attention on contributions dealing with practical applications, in which hand-crafted features still play a key role and achieve state-of-the-art performances, and where deep features are used in conjunction with specific methods for improving their interpretability.

All interested authors are invited to submit their newest results on biomedical image processing and analysis for possible publication in this Special Issue. All papers need to present original, previously unpublished work, and will be subject to the normal standards and peer-review processes of this journal. Potential topics include, but are not limited to:

Supervised segmentation;
Weakly-supervised segmentation;
Self-supervised segmentation;
Supervised detection;
Weakly-supervised detection;
Self-supervised detection;
Deep features for biomedical image classification;
Handcrafted features for biomedical image classification;
Medical image indexing and retrieval;
Medical image classification;
Computer-aided detection/diagnosis applications;
Machine learning and artificial intelligence in CAD.

Keywords

Deep learning
Machine learning
Transfer learning
Ensemble learning
artificial intelligence
image processing
Medical image processing
biomedical imaging
image classification
Convolutional Neural Networks
CNN
Neural Networks
Image indexing
Medical image retrieval
Medical image classification
Histology image analysis
Blood image analysis
Biomedical image classification
Feature extraction
Statistical methods
Orthogonal moments
Deep features for biomedical image classification
Handcrafted features for biomedical image classification…

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Published Papers (4 papers)

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Research

16 pages, 4537 KiB

Open AccessArticle

Cat Swarm Optimization-Based Computer-Aided Diagnosis Model for Lung Cancer Classification in Computed Tomography Images

by Thavavel Vaiyapuri, Liyakathunisa, Haya Alaskar, Ramasubramanian Parvathi, Venkatasubbu Pattabiraman and Abir Hussain

Appl. Sci. 2022, 12(11), 5491; https://doi.org/10.3390/app12115491 - 28 May 2022

Cited by 22 | Viewed by 2259

Abstract

Lung cancer is the most significant cancer that heavily contributes to cancer-related mortality rate, due to its violent nature and late diagnosis at advanced stages. Early identification of lung cancer is essential for improving the survival rate. Various imaging modalities, including X-rays and computed tomography (CT) scans, are employed to diagnose lung cancer. Computer-aided diagnosis (CAD) models are necessary for minimizing the burden upon radiologists and enhancing detection efficiency. Currently, computer vision (CV) and deep learning (DL) models are employed to detect and classify the lung cancer in a precise manner. In this background, the current study presents a cat swarm optimization-based computer-aided diagnosis model for lung cancer classification (CSO-CADLCC) model. The proposed CHO-CADLCC technique initially pre-process the data using the Gabor filtering-based noise removal technique. Furthermore, feature extraction of the pre-processed images is performed with the help of NASNetLarge model. This model is followed by the CSO algorithm with weighted extreme learning machine (WELM) model, which is exploited for lung nodule classification. Finally, the CSO algorithm is utilized for optimal parameter tuning of the WELM model, resulting in an improved classification performance. The experimental validation of the proposed CSO-CADLCC technique was conducted against a benchmark dataset, and the results were assessed under several aspects. The experimental outcomes established the promising performance of the CSO-CADLCC approach over recent approaches under different measures. Full article

(This article belongs to the Special Issue Computer Aided Diagnosis)

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19 pages, 1474 KiB

Open AccessArticle

A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification

by Débora N. Diniz, Mariana T. Rezende, Andrea G. C. Bianchi, Claudia M. Carneiro, Daniela M. Ushizima, Fátima N. S. de Medeiros and Marcone J. F. Souza

Appl. Sci. 2021, 11(9), 4091; https://doi.org/10.3390/app11094091 - 30 Apr 2021

Cited by 25 | Viewed by 4497

Abstract

Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods. Full article

(This article belongs to the Special Issue Computer Aided Diagnosis)

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11 pages, 1090 KiB

Open AccessArticle

Pathomics and Deep Learning Classification of a Heterogeneous Fluorescence Histology Image Dataset

by Georgios S. Ioannidis, Eleftherios Trivizakis, Ioannis Metzakis, Stilianos Papagiannakis, Eleni Lagoudaki and Kostas Marias

Appl. Sci. 2021, 11(9), 3796; https://doi.org/10.3390/app11093796 - 22 Apr 2021

Cited by 11 | Viewed by 3613

Abstract

Automated pathology image classification through modern machine learning (ML) techniques in quantitative microscopy is an emerging AI application area aiming to alleviate the increased workload of pathologists and improve diagnostic accuracy and consistency. However, there are very few efforts focusing on fluorescence histology image data, which is a challenging task, not least due to the variable imaging acquisition parameters in pooled data, which can diminish the performance of ML-based decision support tools. To this end, this study introduces a harmonization preprocessing protocol for image classification within a heterogeneous fluorescence dataset in terms of image acquisition parameters and presents two state-of-the-art feature-based approaches for differentiating three classes of nuclei labelled by an expert based on (a) pathomics analysis scoring an accuracy (ACC) up to 0.957 ± 0.105, and, (b) transfer learning model exhibiting ACC up-to 0.951 ± 0.05. The proposed analysis pipelines offer good differentiation performance in the examined fluorescence histology image dataset despite the heterogeneity due to the lack of a standardized image acquisition protocol. Full article

(This article belongs to the Special Issue Computer Aided Diagnosis)

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33 pages, 10856 KiB

Open AccessArticle

Classification of Shoulder X-ray Images with Deep Learning Ensemble Models

by Fatih Uysal, Fırat Hardalaç, Ozan Peker, Tolga Tolunay and Nil Tokgöz

Appl. Sci. 2021, 11(6), 2723; https://doi.org/10.3390/app11062723 - 18 Mar 2021

Cited by 35 | Viewed by 8489

Abstract

Fractures occur in the shoulder area, which has a wider range of motion than other joints in the body, for various reasons. To diagnose these fractures, data gathered from X-radiation (X-ray), magnetic resonance imaging (MRI), or computed tomography (CT) are used. This study aims to help physicians by classifying shoulder images taken from X-ray devices as fracture/non-fracture with artificial intelligence. For this purpose, the performances of 26 deep learning-based pre-trained models in the detection of shoulder fractures were evaluated on the musculoskeletal radiographs (MURA) dataset, and two ensemble learning models (EL1 and EL2) were developed. The pre-trained models used are ResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet, and their spinal fully connected (Spinal FC) versions. In the EL1 and EL2 models developed using pre-trained models with the best performance, test accuracy was 0.8455, 0.8472, Cohen’s kappa was 0.6907, 0.6942 and the area that was related with fracture class under the receiver operating characteristic (ROC) curve (AUC) was 0.8862, 0.8695. As a result of 28 different classifications in total, the highest test accuracy and Cohen’s kappa values were obtained in the EL2 model, and the highest AUC value was obtained in the EL1 model. Full article

(This article belongs to the Special Issue Computer Aided Diagnosis)

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