Analysis of Types of Skin Lesions and Diseases in Everyday Infectious Disease Practice—How Experienced Are We?

Skuhala, Tomislava; Trkulja, Vladimir; Rimac, Marin; Dragobratović, Anja; Desnica, Boško

doi:10.3390/life12070978

Cited by 4 publications

(3 citation statements)

References 51 publications

(73 reference statements)

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“…2, several examples are presented from the ISIC 2018 dataset. The dataset used in this study comprises images of 7 different types of skin diseases [34], including Actinic keratoses, Basal cell carcinoma, Benign keratosis-like lesions, Dermatofibroma, Melanoma, Melanocytic nevi, and Vascular lesions. Moreover, alongside the provided images, the dataset used encompasses essential clinical information pertaining to each picture, encompassing age, gender, localization, and Dx_type (denoting the modality through which the skin disease was diagnosed).…”

Section: B Isic 2018 Dataset Descriptionmentioning

confidence: 99%

Enhancing Skin Diseases Classification Through Dual Ensemble Learning and Pre-trained CNNs

Gannour¹,

Hamida²,

Lamalem³

et al. 2023

IJACSA

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Skin diseases represent a variety of disorders that can affect the skin. In fact, early diagnosis plays a central role in the treatment of this type of disease. This scholarly article introduces a novel approach to classifying skin diseases by leveraging two ensemble learning techniques, encompassing multi-modal and multi-task methodologies. The proposed classifier integrates diverse information sources, including skin lesion images and patient-specific data, aiming to enhance the accuracy of disease classification. By simultaneously utilizing image input and structured data input, the multi-task functionality of the classifier enables efficient disease classification. The integration of multi-modal and multi-task techniques allows for a comprehensive analysis of skin diseases, leading to improved classification performance and a more holistic understanding of the underlying factors influencing disease diagnosis. The efficacy of the classifier was assessed using the ISIC 2018 dataset, which comprises both image and clinical information for each patient with skin diseases. The dataset used in this study comprises images of seven different types of skin diseases and their associated medical information. The findings of our proposed approach show that it outperforms traditional single-modal and single-task classifiers. The results of this study demonstrate that the proposed model attained an accuracy of 97.66% for the initial classification task (image classification). Additionally, the second classification task (clinical data classification) achieved an accuracy of 94.40%.

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Section: B Isic 2018 Dataset Descriptionmentioning

confidence: 99%

Enhancing Skin Diseases Classification Through Dual Ensemble Learning and Pre-trained CNNs

Gannour¹,

Hamida²,

Lamalem³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…These skin infections are spread further, and their severity may create health issues like fever and pain or without pain [69]. In some cases, symptoms of skin diseases are rashes; these rashes are used to diagnose certain diseases like rubella, measles, chickenpox, and erythema infectiosum [58].…”

Section: Overview Of Skin Layers and Theirmentioning

confidence: 99%

Analysis, Prediction and Classification of Skin Cancer using Artificial Intelligence - A Brief Study and Review

Pandala,

S. Periyanayagi

2023

SCPE

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World Health Organization (WHO) records that skin cancer has vigorously affected people in recent decades. Worldwide, many people are affected by skin cancer, and its affected count will increase yearly. Hence, skin cancer has become a threatening disease. Skin cancer prediction at an earlier time is becoming the higher priority and most challenging task worldwide. A computer-based diagnosis is needed to perform the automatic prognosis of skin cancer. It assists dermatologists in many ways, including the prediction of skin cancer at the earlier stages, easy to diagnose and effective. Nowadays, artificial intelligence based machine learning approaches have been implemented for an early prediction of cancer in the skin through medical images. This paper is focused on a detailed, comprehensive review of skin cancer analysis, forecast, and algorithmic-based procedures for classifying skin diseases. Moreover, this review paper focused on various stages of algorithm approaches for skin tumor detection like pre-processing data, segmenting data, feature selection, and disease classifier. This detailed review of neoplasm diseases like cancer on the skin is done based on machine and deep learning algorithms to help further research.

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“…Therefore, accurate diagnosis of skin lesions is crucial for timely and effective treatment [13][14][15]. However, diagnosing skin lesions presents challenges due to reliance on expert visual analysis of dermoscopy images, which suffers from interobserver variability and subjective interpretation [16,17]. Additionally, the high resolution and heterogeneity of skin lesions, along with factors like hair causing clutter, further complicate dermoscopy diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Two-step hierarchical binary classification of cancerous skin lesions using transfer learning and the random forest algorithm

Suleiman,

Anyimadu,

Permana

et al. 2024

Vis. Comput. Ind. Biomed. Art

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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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Analysis of Types of Skin Lesions and Diseases in Everyday Infectious Disease Practice—How Experienced Are We?

Cited by 4 publications

References 51 publications

Enhancing Skin Diseases Classification Through Dual Ensemble Learning and Pre-trained CNNs

Enhancing Skin Diseases Classification Through Dual Ensemble Learning and Pre-trained CNNs

Analysis, Prediction and Classification of Skin Cancer using Artificial Intelligence - A Brief Study and Review

Two-step hierarchical binary classification of cancerous skin lesions using transfer learning and the random forest algorithm

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