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Artificial Intelligence in the Detection and Classification of Skin Diseases

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 38496

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Dr. Tallha Akram

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Guest Editor

Department of Electrical Engineering, Wah Campus, COMSATS University Islamabad, Islamabad 45550, Pakistan
Interests: deep learning; computer vision; applied optimization; machine learning; federated learning

Special Issue Information

Dear Colleagues,

This Special Issue focuses on new artificial intelligence (AI) developments for skin disease detection and classification. Malignant melanoma, one of the worst types of skin cancer, is frequently discovered in people with fair skin. The severity of the skin pigmented cancer and its resulting fatality could be curtailed if it is detected and cured at the early stages of inception. Identifying benign and malignant lesions is of utmost precedence for binary problems. In contrast, the latest publicly available dataset comprises more than two classes. New AI models might be utilised to revamp, strategise and examine the reconstructed skin images to build automatic detection and classification applications. The current expansion of AI in medicine thrives on efficient classification outcomes. The available AI techniques have achieved accuracy to compete with a dermatologist’s physical inspection results.

Furthermore, the detection and taxonomy of skin lesions from dermoscopic and non-dermoscopic images rendered numerous anticipations in this area. The availability of online cloud-based systems, offline estimation resources and the publicly available extensive datasets supported AI technologies. Now, image examination before their practical use in the clinical setting is accessible, such as quality measures of lesions, generating image datasets, the generalisation of models, the relevancy of algorithms in a real-world setting or transparency of determining AI procedures.

According to the focus of the Special Issue, we invite research manuscripts on topics of advances in the use of artificial intelligence approaches for various skin image detection tasks. Options are to detect and classify the skin lesion, its segmentation, efficient AI system analysis of skin images and vital post- or preprocessing procedures to improve skin disease detection.

Dr. Tallha Akram
Guest Editor

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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. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

federated learning in skin cancer classification
semantic segmentation
FPGA with deep learning for medical imaging
transfer learning in deep learning for skin cancer segmentation and classification
skin cancer classification using deep learning
autoencoder-based feature selection using deep learning with application in medical imaging
fusion of convolutional layers in deep learning for recognition
optimal deep learning feature selection for recognition
fusion of image modality using deep learning
non-invasive methods for skin cancer classification

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Published Papers (9 papers)

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Research

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22 pages, 5155 KiB

Open AccessArticle

MSRNet: Multiclass Skin Lesion Recognition Using Additional Residual Block Based Fine-Tuned Deep Models Information Fusion and Best Feature Selection

by Sobia Bibi, Muhammad Attique Khan, Jamal Hussain Shah, Robertas Damaševičius, Areej Alasiry, Mehrez Marzougui, Majed Alhaisoni and Anum Masood

Diagnostics 2023, 13(19), 3063; https://doi.org/10.3390/diagnostics13193063 - 26 Sep 2023

Cited by 44 | Viewed by 2805

Abstract

Cancer is one of the leading significant causes of illness and chronic disease worldwide. Skin cancer, particularly melanoma, is becoming a severe health problem due to its rising prevalence. The considerable death rate linked with melanoma requires early detection to receive immediate and successful treatment. Lesion detection and classification are more challenging due to many forms of artifacts such as hairs, noise, and irregularity of lesion shape, color, irrelevant features, and textures. In this work, we proposed a deep-learning architecture for classifying multiclass skin cancer and melanoma detection. The proposed architecture consists of four core steps: image preprocessing, feature extraction and fusion, feature selection, and classification. A novel contrast enhancement technique is proposed based on the image luminance information. After that, two pre-trained deep models, DarkNet-53 and DensNet-201, are modified in terms of a residual block at the end and trained through transfer learning. In the learning process, the Genetic algorithm is applied to select hyperparameters. The resultant features are fused using a two-step approach named serial-harmonic mean. This step increases the accuracy of the correct classification, but some irrelevant information is also observed. Therefore, an algorithm is developed to select the best features called marine predator optimization (MPA) controlled Reyni Entropy. The selected features are finally classified using machine learning classifiers for the final classification. Two datasets, ISIC2018 and ISIC2019, have been selected for the experimental process. On these datasets, the obtained maximum accuracy of 85.4% and 98.80%, respectively. To prove the effectiveness of the proposed methods, a detailed comparison is conducted with several recent techniques and shows the proposed framework outperforms. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection and Classification of Skin Diseases)

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25 pages, 7980 KiB

Open AccessArticle

SkinNet-INIO: Multiclass Skin Lesion Localization and Classification Using Fusion-Assisted Deep Neural Networks and Improved Nature-Inspired Optimization Algorithm

by Muneezah Hussain, Muhammad Attique Khan, Robertas Damaševičius, Areej Alasiry, Mehrez Marzougui, Majed Alhaisoni and Anum Masood

Diagnostics 2023, 13(18), 2869; https://doi.org/10.3390/diagnostics13182869 - 6 Sep 2023

Cited by 35 | Viewed by 2719

Abstract

Background: Using artificial intelligence (AI) with the concept of a deep learning-based automated computer-aided diagnosis (CAD) system has shown improved performance for skin lesion classification. Although deep convolutional neural networks (DCNNs) have significantly improved many image classification tasks, it is still difficult to accurately classify skin lesions because of a lack of training data, inter-class similarity, intra-class variation, and the inability to concentrate on semantically significant lesion parts. Innovations: To address these issues, we proposed an automated deep learning and best feature selection framework for multiclass skin lesion classification in dermoscopy images. The proposed framework performs a preprocessing step at the initial step for contrast enhancement using a new technique that is based on dark channel haze and top–bottom filtering. Three pre-trained deep learning models are fine-tuned in the next step and trained using the transfer learning concept. In the fine-tuning process, we added and removed a few additional layers to lessen the parameters and later selected the hyperparameters using a genetic algorithm (GA) instead of manual assignment. The purpose of hyperparameter selection using GA is to improve the learning performance. After that, the deeper layer is selected for each network and deep features are extracted. The extracted deep features are fused using a novel serial correlation-based approach. This technique reduces the feature vector length to the serial-based approach, but there is little redundant information. We proposed an improved anti-Lion optimization algorithm for the best feature selection to address this issue. The selected features are finally classified using machine learning algorithms. Main Results: The experimental process was conducted using two publicly available datasets, ISIC2018 and ISIC2019. Employing these datasets, we obtained an accuracy of 96.1 and 99.9%, respectively. Comparison was also conducted with state-of-the-art techniques and shows the proposed framework improved accuracy. Conclusions: The proposed framework successfully enhances the contrast of the cancer region. Moreover, the selection of hyperparameters using the automated techniques improved the learning process of the proposed framework. The proposed fusion and improved version of the selection process maintains the best accuracy and shorten the computational time. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection and Classification of Skin Diseases)

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18 pages, 5957 KiB

Open AccessArticle

Precision in Dermatology: Developing an Optimal Feature Selection Framework for Skin Lesion Classification

by Tallha Akram, Riaz Junejo, Anas Alsuhaibani, Muhammad Rafiullah, Adeel Akram and Nouf Abdullah Almujally

Diagnostics 2023, 13(17), 2848; https://doi.org/10.3390/diagnostics13172848 - 2 Sep 2023

Cited by 4 | Viewed by 2024

Abstract

Melanoma is widely recognized as one of the most lethal forms of skin cancer, with its incidence showing an upward trend in recent years. Nonetheless, the timely detection of this malignancy substantially enhances the likelihood of patients’ long-term survival. Several computer-based methods have recently been proposed, in the pursuit of diagnosing skin lesions at their early stages. Despite achieving some level of success, there still remains a margin of error that the machine learning community considers to be an unresolved research challenge. The primary objective of this study was to maximize the input feature information by combining multiple deep models in the first phase, and then to avoid noisy and redundant information by downsampling the feature set, using a novel evolutionary feature selection technique, in the second phase. By maintaining the integrity of the original feature space, the proposed idea generated highly discriminant feature information. Recent deep models, including Darknet53, DenseNet201, InceptionV3, and InceptionResNetV2, were employed in our study, for the purpose of feature extraction. Additionally, transfer learning was leveraged, to enhance the performance of our approach. In the subsequent phase, the extracted feature information from the chosen pre-existing models was combined, with the aim of preserving maximum information, prior to undergoing the process of feature selection, using a novel entropy-controlled gray wolf optimization (ECGWO) algorithm. The integration of fusion and selection techniques was employed, initially to incorporate the feature vector with a high level of information and, subsequently, to eliminate redundant and irrelevant feature information. The effectiveness of our concept is supported by an assessment conducted on three benchmark dermoscopic datasets: PH2, ISIC-MSK, and ISIC-UDA. In order to validate the proposed methodology, a comprehensive evaluation was conducted, including a rigorous comparison to established techniques in the field. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection and Classification of Skin Diseases)

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11 pages, 1759 KiB

Open AccessArticle

Privacy-Aware Collaborative Learning for Skin Cancer Prediction

by Qurat ul Ain, Muhammad Amir Khan, Muhammad Mateen Yaqoob, Umar Farooq Khattak, Zohaib Sajid, Muhammad Ijaz Khan and Amal Al-Rasheed

Diagnostics 2023, 13(13), 2264; https://doi.org/10.3390/diagnostics13132264 - 4 Jul 2023

Cited by 4 | Viewed by 2450

Abstract

Cancer, including the highly dangerous melanoma, is marked by uncontrolled cell growth and the possibility of spreading to other parts of the body. However, the conventional approach to machine learning relies on centralized training data, posing challenges for data privacy in healthcare systems driven by artificial intelligence. The collection of data from diverse sensors leads to increased computing costs, while privacy restrictions make it challenging to employ traditional machine learning methods. Researchers are currently confronted with the formidable task of developing a skin cancer prediction technique that takes privacy concerns into account while simultaneously improving accuracy. In this work, we aimed to propose a decentralized privacy-aware learning mechanism to accurately predict melanoma skin cancer. In this research we analyzed federated learning from the skin cancer database. The results from the study showed that 92% accuracy was achieved by the proposed method, which was higher than baseline algorithms. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection and Classification of Skin Diseases)

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15 pages, 2744 KiB

Open AccessArticle

Federated Machine Learning for Skin Lesion Diagnosis: An Asynchronous and Weighted Approach

by Muhammad Mateen Yaqoob, Musleh Alsulami, Muhammad Amir Khan, Deafallah Alsadie, Abdul Khader Jilani Saudagar and Mohammed AlKhathami

Diagnostics 2023, 13(11), 1964; https://doi.org/10.3390/diagnostics13111964 - 5 Jun 2023

Cited by 19 | Viewed by 2820

Abstract

The accurate and timely diagnosis of skin cancer is crucial as it can be a life-threatening disease. However, the implementation of traditional machine learning algorithms in healthcare settings is faced with significant challenges due to data privacy concerns. To tackle this issue, we propose a privacy-aware machine learning approach for skin cancer detection that utilizes asynchronous federated learning and convolutional neural networks (CNNs). Our method optimizes communication rounds by dividing the CNN layers into shallow and deep layers, with the shallow layers being updated more frequently. In order to enhance the accuracy and convergence of the central model, we introduce a temporally weighted aggregation approach that takes advantage of previously trained local models. Our approach is evaluated on a skin cancer dataset, and the results show that it outperforms existing methods in terms of accuracy and communication cost. Specifically, our approach achieves a higher accuracy rate while requiring fewer communication rounds. The results suggest that our proposed method can be a promising solution for improving skin cancer diagnosis while also addressing data privacy concerns in healthcare settings. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection and Classification of Skin Diseases)

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15 pages, 5991 KiB

Open AccessArticle

Anti-Aliasing Attention U-net Model for Skin Lesion Segmentation

by Phuong Thi Le, Bach-Tung Pham, Ching-Chun Chang, Yi-Chiung Hsu, Tzu-Chiang Tai, Yung-Hui Li and Jia-Ching Wang

Diagnostics 2023, 13(8), 1460; https://doi.org/10.3390/diagnostics13081460 - 18 Apr 2023

Cited by 12 | Viewed by 2994

Abstract

The need for a lightweight and reliable segmentation algorithm is critical in various biomedical image-prediction applications. However, the limited quantity of data presents a significant challenge for image segmentation. Additionally, low image quality negatively impacts the efficiency of segmentation, and previous deep learning models for image segmentation require large parameters with hundreds of millions of computations, resulting in high costs and processing times. In this study, we introduce a new lightweight segmentation model, the mobile anti-aliasing attention u-net model (MAAU), which features both encoder and decoder paths. The encoder incorporates an anti-aliasing layer and convolutional blocks to reduce the spatial resolution of input images while avoiding shift equivariance. The decoder uses an attention block and decoder module to capture prominent features in each channel. To address data-related problems, we implemented data augmentation methods such as flip, rotation, shear, translate, and color distortions, which enhanced segmentation efficiency in the international Skin Image Collaboration (ISIC) 2018 and PH2 datasets. Our experimental results demonstrated that our approach had fewer parameters, only 4.2 million, while it outperformed various state-of-the-art segmentation methods. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection and Classification of Skin Diseases)

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35 pages, 6025 KiB

Open AccessArticle

Light-Dermo: A Lightweight Pretrained Convolution Neural Network for the Diagnosis of Multiclass Skin Lesions

by Abdul Rauf Baig, Qaisar Abbas, Riyad Almakki, Mostafa E. A. Ibrahim, Lulwah AlSuwaidan and Alaa E. S. Ahmed

Diagnostics 2023, 13(3), 385; https://doi.org/10.3390/diagnostics13030385 - 19 Jan 2023

Cited by 21 | Viewed by 3011

Abstract

Skin cancer develops due to the unusual growth of skin cells. Early detection is critical for the recognition of multiclass pigmented skin lesions (PSLs). At an early stage, the manual work by ophthalmologists takes time to recognize the PSLs. Therefore, several “computer-aided diagnosis (CAD)” systems are developed by using image processing, machine learning (ML), and deep learning (DL) techniques. Deep-CNN models outperformed traditional ML approaches in extracting complex features from PSLs. In this study, a special transfer learning (TL)-based CNN model is suggested for the diagnosis of seven classes of PSLs. A novel approach (Light-Dermo) is developed that is based on a lightweight CNN model and applies the channelwise attention (CA) mechanism with a focus on computational efficiency. The ShuffleNet architecture is chosen as the backbone, and squeeze-and-excitation (SE) blocks are incorporated as the technique to enhance the original ShuffleNet architecture. Initially, an accessible dataset with 14,000 images of PSLs from seven classes is used to validate the Light-Dermo model. To increase the size of the dataset and control its imbalance, we have applied data augmentation techniques to seven classes of PSLs. By applying this technique, we collected 28,000 images from the HAM10000, ISIS-2019, and ISIC-2020 datasets. The outcomes of the experiments show that the suggested approach outperforms compared techniques in many cases. The most accurately trained model has an accuracy of 99.14%, a specificity of 98.20%, a sensitivity of 97.45%, and an F1-score of 98.1%, with fewer parameters compared to state-of-the-art DL models. The experimental results show that Light-Dermo assists the dermatologist in the better diagnosis of PSLs. The Light-Dermo code is available to the public on GitHub so that researchers can use it and improve it. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection and Classification of Skin Diseases)

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14 pages, 1596 KiB

Open AccessArticle

A Hybrid Preprocessor DE-ABC for Efficient Skin-Lesion Segmentation with Improved Contrast

by Shairyar Malik, Tallha Akram, Imran Ashraf, Muhammad Rafiullah, Mukhtar Ullah and Jawad Tanveer

Diagnostics 2022, 12(11), 2625; https://doi.org/10.3390/diagnostics12112625 - 29 Oct 2022

Cited by 11 | Viewed by 2157

Abstract

Rapid advancements and the escalating necessity of autonomous algorithms in medical imaging require efficient models to accomplish tasks such as segmentation and classification. However, there exists a significant dependency on the image quality of datasets when using these models. Appreciable improvements to enhance datasets for efficient image analysis have been noted in the past. In addition, deep learning and machine learning are vastly employed in this field. However, even after the advent of these advanced techniques, a significant space exists for new research. Recent research works indicate the vast applicability of preprocessing techniques in segmentation tasks. Contrast stretching is one of the preprocessing techniques used to enhance a region of interest. We propose a novel hybrid meta-heuristic preprocessor (DE-ABC), which optimises the decision variables used in the contrast-enhancement transformation function. We validated the efficiency of the preprocessor against some state-of-the-art segmentation algorithms. Publicly available skin-lesion datasets such as

P H^{2},

ISIC-2016, ISIC-2017, and ISIC-2018 were employed. We used Jaccard and the dice coefficient as performance matrices; at the maximum, the proposed model improved the dice coefficient from 93.56% to 94.09%. Cross-comparisons of segmentation results with the original datasets versus the contrast-stretched datasets validate that DE-ABC enhances the efficiency of segmentation algorithms. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection and Classification of Skin Diseases)

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Review

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30 pages, 6686 KiB

Open AccessReview

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

by Junpeng Zhang, Fan Zhong, Kaiqiao He, Mengqi Ji, Shuli Li and Chunying Li

Diagnostics 2023, 13(23), 3506; https://doi.org/10.3390/diagnostics13233506 - 22 Nov 2023

Cited by 21 | Viewed by 14951

Abstract

Objective: Skin diseases constitute a widespread health concern, and the application of machine learning and deep learning algorithms has been instrumental in improving diagnostic accuracy and treatment effectiveness. This paper aims to provide a comprehensive review of the existing research on the utilization of machine learning and deep learning in the field of skin disease diagnosis, with a particular focus on recent widely used methods of deep learning. The present challenges and constraints were also analyzed and possible solutions were proposed. Methods: We collected comprehensive works from the literature, sourced from distinguished databases including IEEE, Springer, Web of Science, and PubMed, with a particular emphasis on the most recent 5-year advancements. From the extensive corpus of available research, twenty-nine articles relevant to the segmentation of dermatological images and forty-five articles about the classification of dermatological images were incorporated into this review. These articles were systematically categorized into two classes based on the computational algorithms utilized: traditional machine learning algorithms and deep learning algorithms. An in-depth comparative analysis was carried out, based on the employed methodologies and their corresponding outcomes. Conclusions: Present outcomes of research highlight the enhanced effectiveness of deep learning methods over traditional machine learning techniques in the field of dermatological diagnosis. Nevertheless, there remains significant scope for improvement, especially in improving the accuracy of algorithms. The challenges associated with the availability of diverse datasets, the generalizability of segmentation and classification models, and the interpretability of models also continue to be pressing issues. Moreover, the focus of future research should be appropriately shifted. A significant amount of existing research is primarily focused on melanoma, and consequently there is a need to broaden the field of pigmented dermatology research in the future. These insights not only emphasize the potential of deep learning in dermatological diagnosis but also highlight directions that should be focused on. Full article

(This article belongs to the Special Issue Artificial Intelligence in the Detection and Classification of Skin Diseases)

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