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Lyakhova UA, Lyakhov PA. Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects. Comput Biol Med 2024; 178:108742. [PMID: 38875908 DOI: 10.1016/j.compbiomed.2024.108742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 06/03/2024] [Accepted: 06/08/2024] [Indexed: 06/16/2024]
Abstract
In recent years, there has been a significant improvement in the accuracy of the classification of pigmented skin lesions using artificial intelligence algorithms. Intelligent analysis and classification systems are significantly superior to visual diagnostic methods used by dermatologists and oncologists. However, the application of such systems in clinical practice is severely limited due to a lack of generalizability and risks of potential misclassification. Successful implementation of artificial intelligence-based tools into clinicopathological practice requires a comprehensive study of the effectiveness and performance of existing models, as well as further promising areas for potential research development. The purpose of this systematic review is to investigate and evaluate the accuracy of artificial intelligence technologies for detecting malignant forms of pigmented skin lesions. For the study, 10,589 scientific research and review articles were selected from electronic scientific publishers, of which 171 articles were included in the presented systematic review. All selected scientific articles are distributed according to the proposed neural network algorithms from machine learning to multimodal intelligent architectures and are described in the corresponding sections of the manuscript. This research aims to explore automated skin cancer recognition systems, from simple machine learning algorithms to multimodal ensemble systems based on advanced encoder-decoder models, visual transformers (ViT), and generative and spiking neural networks. In addition, as a result of the analysis, future directions of research, prospects, and potential for further development of automated neural network systems for classifying pigmented skin lesions are discussed.
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Affiliation(s)
- U A Lyakhova
- Department of Mathematical Modeling, North-Caucasus Federal University, 355017, Stavropol, Russia.
| | - P A Lyakhov
- Department of Mathematical Modeling, North-Caucasus Federal University, 355017, Stavropol, Russia; North-Caucasus Center for Mathematical Research, North-Caucasus Federal University, 355017, Stavropol, Russia.
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Ramamurthy K, Thayumanaswamy I, Radhakrishnan M, Won D, Lingaswamy S. Integration of Localized, Contextual, and Hierarchical Features in Deep Learning for Improved Skin Lesion Classification. Diagnostics (Basel) 2024; 14:1338. [PMID: 39001229 PMCID: PMC11241006 DOI: 10.3390/diagnostics14131338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 07/16/2024] Open
Abstract
Skin lesion classification is vital for the early detection and diagnosis of skin diseases, facilitating timely intervention and treatment. However, existing classification methods face challenges in managing complex information and long-range dependencies in dermoscopic images. Therefore, this research aims to enhance the feature representation by incorporating local, global, and hierarchical features to improve the performance of skin lesion classification. We introduce a novel dual-track deep learning (DL) model in this research for skin lesion classification. The first track utilizes a modified Densenet-169 architecture that incorporates a Coordinate Attention Module (CoAM). The second track employs a customized convolutional neural network (CNN) comprising a Feature Pyramid Network (FPN) and Global Context Network (GCN) to capture multiscale features and global contextual information. The local features from the first track and the global features from second track are used for precise localization and modeling of the long-range dependencies. By leveraging these architectural advancements within the DenseNet framework, the proposed neural network achieved better performance compared to previous approaches. The network was trained and validated using the HAM10000 dataset, achieving a classification accuracy of 93.2%.
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Affiliation(s)
- Karthik Ramamurthy
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai 600127, India
| | - Illakiya Thayumanaswamy
- Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur 603203, India
| | - Menaka Radhakrishnan
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai 600127, India
| | - Daehan Won
- System Sciences and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
| | - Sindhia Lingaswamy
- Department of Computer Applications, National Institute of Technology, Tiruchirappalli 620015, India
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Suleiman TA, Anyimadu DT, Permana AD, Ngim HAA, Scotto di Freca A. Two-step hierarchical binary classification of cancerous skin lesions using transfer learning and the random forest algorithm. Vis Comput Ind Biomed Art 2024; 7:15. [PMID: 38884841 PMCID: PMC11183002 DOI: 10.1186/s42492-024-00166-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 05/24/2024] [Indexed: 06/18/2024] Open
Abstract
Skin lesion classification plays a crucial role in the early detection and diagnosis of various skin conditions. Recent advances in computer-aided diagnostic techniques have been instrumental in timely intervention, thereby improving patient outcomes, particularly in rural communities lacking specialized expertise. Despite the widespread adoption of convolutional neural networks (CNNs) in skin disease detection, their effectiveness has been hindered by the limited size and data imbalance of publicly accessible skin lesion datasets. In this context, a two-step hierarchical binary classification approach is proposed utilizing hybrid machine and deep learning (DL) techniques. Experiments conducted on the International Skin Imaging Collaboration (ISIC 2017) dataset demonstrate the effectiveness of the hierarchical approach in handling large class imbalances. Specifically, employing DenseNet121 (DNET) as a feature extractor and random forest (RF) as a classifier yielded the most promising results, achieving a balanced multiclass accuracy (BMA) of 91.07% compared to the pure deep-learning model (end-to-end DNET) with a BMA of 88.66%. The RF ensemble exhibited significantly greater efficiency than other machine-learning classifiers in aiding DL to address the challenge of learning with limited data. Furthermore, the implemented predictive hybrid hierarchical model demonstrated enhanced performance while significantly reducing computational time, indicating its potential efficiency in real-world applications for the classification of skin lesions.
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Affiliation(s)
- Taofik Ahmed Suleiman
- Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, 03043, Italy
| | - Daniel Tweneboah Anyimadu
- Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, 03043, Italy
| | - Andrew Dwi Permana
- Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, 03043, Italy
| | - Hsham Abdalgny Abdalwhab Ngim
- Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, 03043, Italy
| | - Alessandra Scotto di Freca
- Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, 03043, Italy.
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Foltz EA, Witkowski A, Becker AL, Latour E, Lim JY, Hamilton A, Ludzik J. Artificial Intelligence Applied to Non-Invasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer: A Systematic Review. Cancers (Basel) 2024; 16:629. [PMID: 38339380 PMCID: PMC10854803 DOI: 10.3390/cancers16030629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/28/2024] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND The objective of this study is to systematically analyze the current state of the literature regarding novel artificial intelligence (AI) machine learning models utilized in non-invasive imaging for the early detection of nonmelanoma skin cancers. Furthermore, we aimed to assess their potential clinical relevance by evaluating the accuracy, sensitivity, and specificity of each algorithm and assessing for the risk of bias. METHODS Two reviewers screened the MEDLINE, Cochrane, PubMed, and Embase databases for peer-reviewed studies that focused on AI-based skin cancer classification involving nonmelanoma skin cancers and were published between 2018 and 2023. The search terms included skin neoplasms, nonmelanoma, basal-cell carcinoma, squamous-cell carcinoma, diagnostic techniques and procedures, artificial intelligence, algorithms, computer systems, dermoscopy, reflectance confocal microscopy, and optical coherence tomography. Based on the search results, only studies that directly answered the review objectives were included and the efficacy measures for each were recorded. A QUADAS-2 risk assessment for bias in included studies was then conducted. RESULTS A total of 44 studies were included in our review; 40 utilizing dermoscopy, 3 using reflectance confocal microscopy (RCM), and 1 for hyperspectral epidermal imaging (HEI). The average accuracy of AI algorithms applied to all imaging modalities combined was 86.80%, with the same average for dermoscopy. Only one of the three studies applying AI to RCM measured accuracy, with a result of 87%. Accuracy was not measured in regard to AI based HEI interpretation. CONCLUSION AI algorithms exhibited an overall favorable performance in the diagnosis of nonmelanoma skin cancer via noninvasive imaging techniques. Ultimately, further research is needed to isolate pooled diagnostic accuracy for nonmelanoma skin cancers as many testing datasets also include melanoma and other pigmented lesions.
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Affiliation(s)
- Emilie A. Foltz
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA
- Elson S. Floyd College of Medicine, Washington State University, Spokane, WA 99202, USA
| | - Alexander Witkowski
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA
| | - Alyssa L. Becker
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA
- John A. Burns School of Medicine, University of Hawai’i at Manoa, Honolulu, HI 96813, USA
| | - Emile Latour
- Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97201, USA
| | - Jeong Youn Lim
- Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97201, USA
| | - Andrew Hamilton
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA
| | - Joanna Ludzik
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA
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Ayikpa KJ, Gouton P, Mamadou D, Ballo AB. Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm. J Imaging 2024; 10:19. [PMID: 38249004 PMCID: PMC10817301 DOI: 10.3390/jimaging10010019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 12/29/2023] [Accepted: 01/03/2024] [Indexed: 01/23/2024] Open
Abstract
The quality of cocoa beans is crucial in influencing the taste, aroma, and texture of chocolate and consumer satisfaction. High-quality cocoa beans are valued on the international market, benefiting Ivorian producers. Our study uses advanced techniques to evaluate and classify cocoa beans by analyzing spectral measurements, integrating machine learning algorithms, and optimizing parameters through genetic algorithms. The results highlight the critical importance of parameter optimization for optimal performance. Logistic regression, support vector machines (SVM), and random forest algorithms demonstrate a consistent performance. XGBoost shows improvements in the second generation, followed by a slight decrease in the fifth. On the other hand, the performance of AdaBoost is not satisfactory in generations two and five. The results are presented on three levels: first, using all parameters reveals that logistic regression obtains the best performance with a precision of 83.78%. Then, the results of the parameters selected in the second generation still show the logistic regression with the best precision of 84.71%. Finally, the results of the parameters chosen in the second generation place random forest in the lead with a score of 74.12%.
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Affiliation(s)
- Kacoutchy Jean Ayikpa
- Laboratoire Imagerie et Vision Artificielle (ImViA), Université de Bourgogne, 21000 Dijon, France; (K.J.A.); (D.M.); (A.B.B.)
| | - Pierre Gouton
- Laboratoire Imagerie et Vision Artificielle (ImViA), Université de Bourgogne, 21000 Dijon, France; (K.J.A.); (D.M.); (A.B.B.)
| | - Diarra Mamadou
- Laboratoire Imagerie et Vision Artificielle (ImViA), Université de Bourgogne, 21000 Dijon, France; (K.J.A.); (D.M.); (A.B.B.)
| | - Abou Bakary Ballo
- Laboratoire Imagerie et Vision Artificielle (ImViA), Université de Bourgogne, 21000 Dijon, France; (K.J.A.); (D.M.); (A.B.B.)
- Laboratoire de Mécanique et Information (LaMI), Université Felix Houphouët-Boigny, Abidjan 22 BP 801, Côte d’Ivoire
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Gayatri E, Aarthy SL. Reduction of overfitting on the highly imbalanced ISIC-2019 skin dataset using deep learning frameworks. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:53-68. [PMID: 38189730 DOI: 10.3233/xst-230204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
BACKGROUND With the rapid growth of Deep Neural Networks (DNN) and Computer-Aided Diagnosis (CAD), more significant works have been analysed for cancer related diseases. Skin cancer is the most hazardous type of cancer that cannot be diagnosed in the early stages. OBJECTIVE The diagnosis of skin cancer is becoming a challenge to dermatologists as an abnormal lesion looks like an ordinary nevus at the initial stages. Therefore, early identification of lesions (origin of skin cancer) is essential and helpful for treating skin cancer patients effectively. The enormous development of automated skin cancer diagnosis systems significantly supports dermatologists. METHODS This paper performs a classification of skin cancer by utilising various deep-learning frameworks after resolving the class Imbalance problem in the ISIC-2019 dataset. A fine-tuned ResNet-50 model is used to evaluate the performance of original data, augmented data, and after by adding the focal loss. Focal loss is the best technique to solve overfitting problems by assigning weights to hard misclassified images. RESULTS Finally, augmented data with focal loss is given a good classification performance with 98.85% accuracy, 95.52% precision, and 95.93% recall. Matthews Correlation coefficient (MCC) is the best metric to evaluate the quality of multi-class images. It has given outstanding performance by using augmented data and focal loss.
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Affiliation(s)
| | - S L Aarthy
- SCOPE, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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Hossain MM, Hossain MM, Arefin MB, Akhtar F, Blake J. Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble. Diagnostics (Basel) 2023; 14:89. [PMID: 38201399 PMCID: PMC10795598 DOI: 10.3390/diagnostics14010089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024] Open
Abstract
Skin cancer poses a significant healthcare challenge, requiring precise and prompt diagnosis for effective treatment. While recent advances in deep learning have dramatically improved medical image analysis, including skin cancer classification, ensemble methods offer a pathway for further enhancing diagnostic accuracy. This study introduces a cutting-edge approach employing the Max Voting Ensemble Technique for robust skin cancer classification on ISIC 2018: Task 1-2 dataset. We incorporate a range of cutting-edge, pre-trained deep neural networks, including MobileNetV2, AlexNet, VGG16, ResNet50, DenseNet201, DenseNet121, InceptionV3, ResNet50V2, InceptionResNetV2, and Xception. These models have been extensively trained on skin cancer datasets, achieving individual accuracies ranging from 77.20% to 91.90%. Our method leverages the synergistic capabilities of these models by combining their complementary features to elevate classification performance further. In our approach, input images undergo preprocessing for model compatibility. The ensemble integrates the pre-trained models with their architectures and weights preserved. For each skin lesion image under examination, every model produces a prediction. These are subsequently aggregated using the max voting ensemble technique to yield the final classification, with the majority-voted class serving as the conclusive prediction. Through comprehensive testing on a diverse dataset, our ensemble outperformed individual models, attaining an accuracy of 93.18% and an AUC score of 0.9320, thus demonstrating superior diagnostic reliability and accuracy. We evaluated the effectiveness of our proposed method on the HAM10000 dataset to ensure its generalizability. Our ensemble method delivers a robust, reliable, and effective tool for the classification of skin cancer. By utilizing the power of advanced deep neural networks, we aim to assist healthcare professionals in achieving timely and accurate diagnoses, ultimately reducing mortality rates and enhancing patient outcomes.
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Affiliation(s)
- Md. Mamun Hossain
- Department of Computer Science and Engineering, Bangladesh Army University of Science and Technology, Saidpur 5310, Bangladesh
| | - Md. Moazzem Hossain
- Department of Computer Science and Engineering, Bangladesh Army University of Science and Technology, Saidpur 5310, Bangladesh
| | - Most. Binoee Arefin
- Department of Computer Science and Engineering, Bangladesh Army University of Science and Technology, Saidpur 5310, Bangladesh
| | - Fahima Akhtar
- Department of Computer Science and Engineering, Bangladesh Army University of Science and Technology, Saidpur 5310, Bangladesh
| | - John Blake
- School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu 965-8580, Japan
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Brutti F, La Rosa F, Lazzeri L, Benvenuti C, Bagnoni G, Massi D, Laurino M. Artificial Intelligence Algorithms for Benign vs. Malignant Dermoscopic Skin Lesion Image Classification. Bioengineering (Basel) 2023; 10:1322. [PMID: 38002446 PMCID: PMC10669580 DOI: 10.3390/bioengineering10111322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/13/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
In recent decades, the incidence of melanoma has grown rapidly. Hence, early diagnosis is crucial to improving clinical outcomes. Here, we propose and compare a classical image analysis-based machine learning method with a deep learning one to automatically classify benign vs. malignant dermoscopic skin lesion images. The same dataset of 25,122 publicly available dermoscopic images was used to train both models, while a disjointed test set of 200 images was used for the evaluation phase. The training dataset was randomly divided into 10 datasets of 19,932 images to obtain an equal distribution between the two classes. By testing both models on the disjoint set, the deep learning-based method returned accuracy of 85.4 ± 3.2% and specificity of 75.5 ± 7.6%, while the machine learning one showed accuracy and specificity of 73.8 ± 1.1% and 44.5 ± 4.7%, respectively. Although both approaches performed well in the validation phase, the convolutional neural network outperformed the ensemble boosted tree classifier on the disjoint test set, showing better generalization ability. The integration of new melanoma detection algorithms with digital dermoscopic devices could enable a faster screening of the population, improve patient management, and achieve better survival rates.
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Affiliation(s)
- Francesca Brutti
- Institute of Clinical Physiology, National Research Council, 56124 Pisa, Italy; (F.B.); (F.L.R.); (C.B.)
| | - Federica La Rosa
- Institute of Clinical Physiology, National Research Council, 56124 Pisa, Italy; (F.B.); (F.L.R.); (C.B.)
| | - Linda Lazzeri
- Uniti of Dermatologia, Specialist Surgery Area, Department of General Surgery, Livorno Hospital, Azienda Usl Toscana Nord Ovest, 57124 Livorno, Italy; (L.L.); (G.B.)
| | - Chiara Benvenuti
- Institute of Clinical Physiology, National Research Council, 56124 Pisa, Italy; (F.B.); (F.L.R.); (C.B.)
| | - Giovanni Bagnoni
- Uniti of Dermatologia, Specialist Surgery Area, Department of General Surgery, Livorno Hospital, Azienda Usl Toscana Nord Ovest, 57124 Livorno, Italy; (L.L.); (G.B.)
| | - Daniela Massi
- Department of Health Sciences, Section of Pathological Anatomy, University of Florence, 50139 Florence, Italy;
| | - Marco Laurino
- Institute of Clinical Physiology, National Research Council, 56124 Pisa, Italy; (F.B.); (F.L.R.); (C.B.)
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Walid MAA, Mollick S, Shill PC, Baowaly MK, Islam MR, Ahamad MM, Othman MA, Samad MA. Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification. Diagnostics (Basel) 2023; 13:3155. [PMID: 37835898 PMCID: PMC10572954 DOI: 10.3390/diagnostics13193155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 10/15/2023] Open
Abstract
The study utilizes osteosarcoma hematoxylin and the Eosin-stained image dataset, which is unevenly dispersed, and it raises concerns about the potential impact on the overall performance and reliability of any analyses or models derived from the dataset. In this study, a deep-learning-based convolution neural network (CNN) and adapted heterogeneous ensemble-learning-based voting classifier have been proposed to classify osteosarcoma. The proposed methods can also resolve the issue and develop unbiased learning models by introducing an evenly distributed training dataset. Data augmentation is employed to boost the generalization abilities. Six different pre-trained CNN models, namely MobileNetV1, Mo-bileNetV2, ResNetV250, InceptionV2, EfficientNetV2B0, and NasNetMobile, are applied and evaluated in frozen and fine-tuned-based phases. In addition, a novel CNN model and adapted heterogeneous ensemble-learning-based voting classifier developed from the proposed CNN model, fine-tuned NasNetMobile model, and fine-tuned Efficient-NetV2B0 model are also introduced to classify osteosarcoma. The proposed CNN model outperforms other pre-trained models. The Kappa score obtained from the proposed CNN model is 93.09%. Notably, the proposed voting classifier attains the highest Kappa score of 96.50% and outperforms all other models. The findings of this study have practical implications in telemedicine, mobile healthcare systems, and as a supportive tool for medical professionals.
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Affiliation(s)
- Md. Abul Ala Walid
- Department of Computer Science and Engineering, Khulna University of Engineering and Technology, Khulna 9203, Bangladesh; (M.A.A.W.)
- Department of Computer Science and Engineering, Northern University of Business and Technology, Khulna 9100, Bangladesh
| | - Swarnali Mollick
- Department of Computer Science and Engineering, Northern University of Business and Technology, Khulna 9100, Bangladesh
| | - Pintu Chandra Shill
- Department of Computer Science and Engineering, Khulna University of Engineering and Technology, Khulna 9203, Bangladesh; (M.A.A.W.)
| | - Mrinal Kanti Baowaly
- Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (M.K.B.)
| | - Md. Rabiul Islam
- Department of Biomedical Engineering, Islamic University, Kushtia 7003, Bangladesh
| | - Md. Martuza Ahamad
- Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (M.K.B.)
| | - Manal A. Othman
- Medical Education Department, College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
| | - Md Abdus Samad
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea
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Okeibunor JC, Jaca A, Iwu-Jaja CJ, Idemili-Aronu N, Ba H, Zantsi ZP, Ndlambe AM, Mavundza E, Muneene D, Wiysonge CS, Makubalo L. The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review. Front Public Health 2023; 11:1102185. [PMID: 37469694 PMCID: PMC10352788 DOI: 10.3389/fpubh.2023.1102185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 06/19/2023] [Indexed: 07/21/2023] Open
Abstract
Background Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care. Methods We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data. Results Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health. Conclusions Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.
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Affiliation(s)
| | - Anelisa Jaca
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | | | - Ngozi Idemili-Aronu
- Department of Sociology/Anthropology, University of Nigeria, Nsukka, Nigeria
| | - Housseynou Ba
- World Health Organization Regional Office for Africa, Brazzaville, Republic of Congo
| | - Zukiswa Pamela Zantsi
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | - Asiphe Mavis Ndlambe
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | - Edison Mavundza
- World Health Organization Regional Office for Africa, Brazzaville, Republic of Congo
| | | | - Charles Shey Wiysonge
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
- HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Durban, South Africa
| | - Lindiwe Makubalo
- World Health Organization Regional Office for Africa, Brazzaville, Republic of Congo
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Liutkus J, Kriukas A, Stragyte D, Mazeika E, Raudonis V, Galetzka W, Stang A, Valiukeviciene S. Accuracy of a Smartphone-Based Artificial Intelligence Application for Classification of Melanomas, Melanocytic Nevi, and Seborrheic Keratoses. Diagnostics (Basel) 2023; 13:2139. [PMID: 37443533 DOI: 10.3390/diagnostics13132139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023] Open
Abstract
Current artificial intelligence algorithms can classify melanomas at a level equivalent to that of experienced dermatologists. The objective of this study was to assess the accuracy of a smartphone-based "You Only Look Once" neural network model for the classification of melanomas, melanocytic nevi, and seborrheic keratoses. The algorithm was trained using 59,090 dermatoscopic images. Testing was performed on histologically confirmed lesions: 32 melanomas, 35 melanocytic nevi, and 33 seborrheic keratoses. The results of the algorithm's decisions were compared with those of two skilled dermatologists and five beginners in dermatoscopy. The algorithm's sensitivity and specificity for melanomas were 0.88 (0.71-0.96) and 0.87 (0.76-0.94), respectively. The algorithm surpassed the beginner dermatologists, who achieved a sensitivity of 0.83 (0.77-0.87). For melanocytic nevi, the algorithm outclassed each group of dermatologists, attaining a sensitivity of 0.77 (0.60-0.90). The algorithm's sensitivity for seborrheic keratoses was 0.52 (0.34-0.69). The smartphone-based "You Only Look Once" neural network model achieved a high sensitivity and specificity in the classification of melanomas and melanocytic nevi with an accuracy similar to that of skilled dermatologists. However, a bigger dataset is required in order to increase the algorithm's sensitivity for seborrheic keratoses.
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Affiliation(s)
- Jokubas Liutkus
- Department of Skin and Venereal Diseases, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
- Department of Skin and Venereal Diseases, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, 50161 Kaunas, Lithuania
| | - Arturas Kriukas
- Department of Skin and Venereal Diseases, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
- Department of Skin and Venereal Diseases, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, 50161 Kaunas, Lithuania
| | - Dominyka Stragyte
- Department of Skin and Venereal Diseases, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
- Department of Skin and Venereal Diseases, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, 50161 Kaunas, Lithuania
| | - Erikas Mazeika
- Department of Skin and Venereal Diseases, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
- Department of Skin and Venereal Diseases, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, 50161 Kaunas, Lithuania
| | - Vidas Raudonis
- Artificial Intelligence Center, Kaunas University of Technology, 51423 Kaunas, Lithuania
| | - Wolfgang Galetzka
- Institute of Medical Informatics, Biometrics and Epidemiology, University Hospital Essen, 45130 Essen, Germany
| | - Andreas Stang
- Institute of Medical Informatics, Biometrics and Epidemiology, University Hospital Essen, 45130 Essen, Germany
| | - Skaidra Valiukeviciene
- Department of Skin and Venereal Diseases, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
- Department of Skin and Venereal Diseases, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, 50161 Kaunas, Lithuania
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12
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Inneci T, Badem H. Detection of Corneal Ulcer Using a Genetic Algorithm-Based Image Selection and Residual Neural Network. Bioengineering (Basel) 2023; 10:639. [PMID: 37370570 DOI: 10.3390/bioengineering10060639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/20/2023] [Accepted: 05/22/2023] [Indexed: 06/29/2023] Open
Abstract
Corneal ulcer is one of the most devastating eye diseases causing permanent damage. There exist limited soft techniques available for detecting this disease. In recent years, deep neural networks (DNN) have significantly solved numerous classification problems. However, many samples are needed to obtain reasonable classification performance using a DNN with a huge amount of layers and weights. Since collecting a data set with a large number of samples is usually a difficult and time-consuming process, very large-scale pre-trained DNNs, such as the AlexNet, the ResNet and the DenseNet, can be adapted to classify a dataset with a small number of samples, through the utility of transfer learning techniques. Although such pre-trained DNNs produce successful results in some cases, their classification performances can be low due to many parameters, weights and the emergence of redundancy features that repeat themselves in many layers in som cases. The proposed technique removes these unnecessary features by systematically selecting images in the layers using a genetic algorithm (GA). The proposed method has been tested on ResNet on a small-scale dataset which classifies corneal ulcers. According to the results, the proposed method significantly increased the classification performance compared to the classical approaches.
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Affiliation(s)
- Tugba Inneci
- Department of Informatics System, Kahramanmaras Sutcu Imam University, Kahramanmaras 46050, Türkiye
| | - Hasan Badem
- Department of Computer Engineering, Kahramanmaras Sutcu Imam University, Kahramanmaras 46050, Türkiye
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13
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Yilmaz EG, Ece E, Erdem Ö, Eş I, Inci F. A Sustainable Solution to Skin Diseases: Ecofriendly Transdermal Patches. Pharmaceutics 2023; 15:579. [PMID: 36839902 PMCID: PMC9960884 DOI: 10.3390/pharmaceutics15020579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/11/2023] Open
Abstract
Skin is the largest epithelial surface of the human body, with a surface area of 2 m2 for the average adult human. Being an external organ, it is susceptible to more than 3000 potential skin diseases, including injury, inflammation, microbial and viral infections, and skin cancer. Due to its nature, it offers a large accessible site for administrating several medications against these diseases. The dermal and transdermal delivery of such medications are often ensured by utilizing dermal/transdermal patches or microneedles made of biocompatible and biodegradable materials. These tools provide controlled delivery of drugs to the site of action in a rapid and therapeutically effective manner with enhanced diffusivity and minimal side effects. Regrettably, they are usually fabricated using synthetic materials with possible harmful environmental effects. Manufacturing such tools using green synthesis routes and raw materials is hence essential for both ecological and economic sustainability. In this review, natural materials including chitosan/chitin, alginate, keratin, gelatin, cellulose, hyaluronic acid, pectin, and collagen utilized in designing ecofriendly patches will be explored. Their implementation in wound healing, skin cancer, inflammations, and infections will be discussed, and the significance of these studies will be evaluated with future perspectives.
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Affiliation(s)
- Eylul Gulsen Yilmaz
- UNAM—National Nanotechnology Research Center, Bilkent University, Ankara 06800, Turkey
- Institute of Materials Science and Nanotechnology, Bilkent University, Ankara 06800, Turkey
| | - Emre Ece
- UNAM—National Nanotechnology Research Center, Bilkent University, Ankara 06800, Turkey
- Institute of Materials Science and Nanotechnology, Bilkent University, Ankara 06800, Turkey
| | - Özgecan Erdem
- UNAM—National Nanotechnology Research Center, Bilkent University, Ankara 06800, Turkey
| | - Ismail Eş
- UNAM—National Nanotechnology Research Center, Bilkent University, Ankara 06800, Turkey
| | - Fatih Inci
- UNAM—National Nanotechnology Research Center, Bilkent University, Ankara 06800, Turkey
- Institute of Materials Science and Nanotechnology, Bilkent University, Ankara 06800, Turkey
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14
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Magdy A, Hussein H, Abdel-Kader RF, Salam KAE. Performance Enhancement of Skin Cancer Classification Using Computer Vision. IEEE ACCESS 2023; 11:72120-72133. [DOI: 10.1109/access.2023.3294974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
- Ahmed Magdy
- Electrical Engineering Department, Suez Canal University, Ismailia, Egypt
| | - Hadeer Hussein
- Electrical Engineering Department, Suez Canal University, Ismailia, Egypt
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15
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An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset. Diagnostics (Basel) 2022; 12:diagnostics12092115. [PMID: 36140516 PMCID: PMC9497837 DOI: 10.3390/diagnostics12092115] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/24/2022] [Accepted: 08/29/2022] [Indexed: 12/12/2022] Open
Abstract
Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today’s medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient’s death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure. The efficient detection of skin cancer is a challenging task. Therefore, the numbers of skilful dermatologists around the globe are not enough to deal with today’s healthcare. The huge difference between data from various healthcare sector classes leads to data imbalance problems. Due to data imbalance issues, deep learning models are often trained on one class more than others. This study proposes a novel deep learning-based skin cancer detector using an imbalanced dataset. Data augmentation was used to balance various skin cancer classes to overcome the data imbalance. The Skin Cancer MNIST: HAM10000 dataset was employed, which consists of seven classes of skin lesions. Deep learning models are widely used in disease diagnosis through images. Deep learning-based models (AlexNet, InceptionV3, and RegNetY-320) were employed to classify skin cancer. The proposed framework was also tuned with various combinations of hyperparameters. The results show that RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets. The performance of the proposed framework was better than that of conventional methods. The accuracy, F1-score, and ROC curve value obtained with the proposed framework were 91%, 88.1%, and 0.95, which were significantly better than those of the state-of-the-art method, which achieved 85%, 69.3%, and 0.90, respectively. Our proposed framework may assist in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare professionals.
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16
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Ghosh P, Azam S, Quadir R, Karim A, Shamrat FMJM, Bhowmik SK, Jonkman M, Hasib KM, Ahmed K. SkinNet-16: A deep learning approach to identify benign and malignant skin lesions. Front Oncol 2022; 12:931141. [PMID: 36003775 PMCID: PMC9395205 DOI: 10.3389/fonc.2022.931141] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/07/2022] [Indexed: 12/24/2022] Open
Abstract
Skin cancer these days have become quite a common occurrence especially in certain geographic areas such as Oceania. Early detection of such cancer with high accuracy is of utmost importance, and studies have shown that deep learning- based intelligent approaches to address this concern have been fruitful. In this research, we present a novel deep learning- based classifier that has shown promise in classifying this type of cancer on a relevant preprocessed dataset having important features pre-identified through an effective feature extraction method. Skin cancer in modern times has become one of the most ubiquitous types of cancer. Accurate identification of cancerous skin lesions is of vital importance in treating this malady. In this research, we employed a deep learning approach to identify benign and malignant skin lesions. The initial dataset was obtained from Kaggle before several preprocessing steps for hair and background removal, image enhancement, selection of the region of interest (ROI), region-based segmentation, morphological gradient, and feature extraction were performed, resulting in histopathological images data with 20 input features based on geometrical and textural features. A principle component analysis (PCA)-based feature extraction technique was put into action to reduce the dimensionality to 10 input features. Subsequently, we applied our deep learning classifier, SkinNet-16, to detect the cancerous lesion accurately at a very early stage. The highest accuracy was obtained with the Adamax optimizer with a learning rate of 0.006 from the neural network-based model developed in this study. The model also delivered an impressive accuracy of approximately 99.19%.
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Affiliation(s)
- Pronab Ghosh
- Department of Computer Science (CS), Lakehead University, Thunder Bay, ON, Canada
| | - Sami Azam
- College of Engineering, IT and Environment, Charles Darwin University, Darwin, NT, Australia
- *Correspondence: Sami Azam,
| | - Ryana Quadir
- Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Asif Karim
- College of Engineering, IT and Environment, Charles Darwin University, Darwin, NT, Australia
| | - F. M. Javed Mehedi Shamrat
- Department of Computer Science and Engineering, Ahsanullah University of Science & Technology, Dhaka, Bangladesh
| | - Shohag Kumar Bhowmik
- Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Mirjam Jonkman
- College of Engineering, IT and Environment, Charles Darwin University, Darwin, NT, Australia
| | - Khan Md. Hasib
- Department of Computer Science and Engineering, Ahsanullah University of Science & Technology, Dhaka, Bangladesh
| | - Kawsar Ahmed
- Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada
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17
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Benvenuto GA, Colnago M, Dias MA, Negri RG, Silva EA, Casaca W. A Fully Unsupervised Deep Learning Framework for Non-Rigid Fundus Image Registration. Bioengineering (Basel) 2022; 9:bioengineering9080369. [PMID: 36004894 PMCID: PMC9404907 DOI: 10.3390/bioengineering9080369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/31/2022] [Accepted: 08/03/2022] [Indexed: 11/26/2022] Open
Abstract
In ophthalmology, the registration problem consists of finding a geometric transformation that aligns a pair of images, supporting eye-care specialists who need to record and compare images of the same patient. Considering the registration methods for handling eye fundus images, the literature offers only a limited number of proposals based on deep learning (DL), whose implementations use the supervised learning paradigm to train a model. Additionally, ensuring high-quality registrations while still being flexible enough to tackle a broad range of fundus images is another drawback faced by most existing methods in the literature. Therefore, in this paper, we address the above-mentioned issues by introducing a new DL-based framework for eye fundus registration. Our methodology combines a U-shaped fully convolutional neural network with a spatial transformation learning scheme, where a reference-free similarity metric allows the registration without assuming any pre-annotated or artificially created data. Once trained, the model is able to accurately align pairs of images captured under several conditions, which include the presence of anatomical differences and low-quality photographs. Compared to other registration methods, our approach achieves better registration outcomes by just passing as input the desired pair of fundus images.
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Affiliation(s)
- Giovana A. Benvenuto
- Faculty of Science and Technology (FCT), São Paulo State University (UNESP), Presidente Prudente 19060-900, Brazil
| | - Marilaine Colnago
- Institute of Mathematics and Computer Science (ICMC), São Paulo University (USP), São Carlos 13566-590, Brazil
| | - Maurício A. Dias
- Faculty of Science and Technology (FCT), São Paulo State University (UNESP), Presidente Prudente 19060-900, Brazil
| | - Rogério G. Negri
- Science and Technology Institute (ICT), São Paulo State University (UNESP), São José dos Campos 12224-300, Brazil
| | - Erivaldo A. Silva
- Faculty of Science and Technology (FCT), São Paulo State University (UNESP), Presidente Prudente 19060-900, Brazil
| | - Wallace Casaca
- Institute of Biosciences, Letters and Exact Sciences (IBILCE), São Paulo State University (UNESP), São José do Rio Preto 15054-000, Brazil
- Correspondence:
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18
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Mustafa S, Iqbal MW, Rana TA, Jaffar A, Shiraz M, Arif M, Chelloug SA. Entropy and Gaussian Filter-Based Adaptive Active Contour for Segmentation of Skin Lesions. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4348235. [PMID: 35909861 PMCID: PMC9325593 DOI: 10.1155/2022/4348235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 06/13/2022] [Accepted: 06/28/2022] [Indexed: 11/18/2022]
Abstract
Malignant melanoma is considered one of the deadliest skin diseases if ignored without treatment. The mortality rate caused by melanoma is more than two times that of other skin malignancy diseases. These facts encourage computer scientists to find automated methods to discover skin cancers. Nowadays, the analysis of skin images is widely used by assistant physicians to discover the first stage of the disease automatically. One of the challenges the computer science researchers faced when developing such a system is the un-clarity of the existing images, such as noise like shadows, low contrast, hairs, and specular reflections, which complicates detecting the skin lesions in that images. This paper proposes the solution to the problem mentioned earlier using the active contour method. Still, seed selection in the dynamic contour method has the main drawback of where it should start the segmentation process. This paper uses Gaussian filter-based maximum entropy and morphological processing methods to find automatic seed points for active contour. By incorporating this, it can segment the lesion from dermoscopic images automatically. Our proposed methodology tested quantitative and qualitative measures on standard dataset dermis and used to test the proposed method's reliability which shows encouraging results.
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Affiliation(s)
- Saleem Mustafa
- Department of Computer Science, Superior University, Lahore 54600, Pakistan
| | | | - Toqir A. Rana
- Department of Computer Science and IT, The University of Lahore, Lahore 54000, Pakistan
- School of Computer Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
| | - Arfan Jaffar
- Department of Computer Science, Superior University, Lahore 54600, Pakistan
| | - Muhammad Shiraz
- Department of Computer Science, Federal Urdu University of Arts, Science & Technology, Islamabad 44000, Pakistan
| | - Muhammad Arif
- Department of Computer Science and IT, The University of Lahore, Lahore 54000, Pakistan
| | - Samia Allaoua Chelloug
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
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