A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challenges

Rezk, Eman; Haggag, May; Eltorki, Mohamed; El-Dakhakhni, Wael

doi:10.1016/j.health.2023.100259

“…The challenge of imbalanced data arises when training a deep learning model for complex tasks [5]. This imbalanced dataset often leads to a biased or skewed prediction, impacting the model's performance [17] [19] [20]. Employing data augmentation becomes crucial as it can augment the sample size for these imbalanced classes, thereby creating a more balanced dataset.…”

Section: Data Balancingmentioning

confidence: 99%

“…They employed a Convolutional Neural Network (CNN) to detect images and patterns, working through three stages: a convolutional layer, a pooling layer, and a fully connected layer. They used the HAM10000 dataset, which consisted of 10,015 images showcasing seven different skin lesions [10], [16], [17]. The images were resized to 90 X 120 pixels and normalized.…”

Section: Introductionmentioning

confidence: 99%

Integration of Multimodal Data Sources for Enhanced Skin Disease Classification and Cancer Prediction: A Study Leveraging Pre-Trained Models on HAM_10000 Metadata and Squamous Cell Carcinoma (SCC) Images.

Khan,

S.,

Gangodkar

2024

Preprint

0

View full text Add to dashboard Cite

Skin diseases, disorders, and deficiencies are fundamental issues in the human body. Over the last few decades, they have become widely prevalent, often leading to cancer. This spectrum of ailments ranges from benign conditions to potentially life-threatening cancers, posing significant challenges to global health. Accurate classification of these diseases and timely prediction of malignant transformations are crucial for effective diagnosis and treatment planning. In our research paper, we propose an innovative approach that levitation multimodal data integration to enhance the precision of skin disease classification and improve the prediction of cancerous transformations. The study utilizes the HAM_10000 Metadata, a dataset comprising diverse skin lesions, along with 100 high-resolution images of Squamous Cell Carcinoma (SCC). Pre-trained models, specifically multi-model CNNs, are employed to extract intricate patterns and features. By amalgamating information from these heterogeneous sources and implementing advanced machine learning techniques, the results highlight the effectiveness of this integrative methodology. This approach can revolutionize the fields of dermatology and oncology by providing patients with skin diseases accurate, efficient, and early diagnoses of skin disease. Through this method, it was discovered that SCC images might hold a higher priority for skin cancer identification, achieving an accuracy rate of 92% on the original dataset.

show abstract

“…Total numbers of epochs are 60 and batch size is 16 in first round of training and testing. As we know that to improve the performance and accuracy of a model we perform iteration[17]. During each epoch, the model goes through the entire training dataset, updating its weights based on the computed gradients.…”

mentioning

confidence: 99%

Integration of Multimodal Data Sources for Enhanced Skin Disease Classification and Cancer Prediction: A Study Leveraging Pre-Trained Models on HAM_10000 Metadata and Squamous Cell Carcinoma (SCC) Images.

Khan,

S.,

Gangodkar

2024

Preprint

0

View full text Add to dashboard Cite

Skin diseases, disorders, and deficiencies are fundamental issues in the human body. Over the last few decades, they have become widely prevalent, often leading to cancer. This spectrum of ailments ranges from benign conditions to potentially life-threatening cancers, posing significant challenges to global health. Accurate classification of these diseases and timely prediction of malignant transformations are crucial for effective diagnosis and treatment planning. In our research paper, we propose an innovative approach that levitation multimodal data integration to enhance the precision of skin disease classification and improve the prediction of cancerous transformations. The study utilizes the HAM_10000 Metadata, a dataset comprising diverse skin lesions, along with 100 high-resolution images of Squamous Cell Carcinoma (SCC). Pre-trained models, specifically multi-model CNNs, are employed to extract intricate patterns and features. By amalgamating information from these heterogeneous sources and implementing advanced machine learning techniques, the results highlight the effectiveness of this integrative methodology. This approach can revolutionize the fields of dermatology and oncology by providing patients with skin diseases accurate, efficient, and early diagnoses of skin disease. Through this method, it was discovered that SCC images might hold a higher priority for skin cancer identification, achieving an accuracy rate of 92% on the original dataset.

show abstract

Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects

Lyakhova,

Lyakhov

2024

Computers in Biology and Medicine

5

0

View full text Add to dashboard Cite

A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challenges

Cited by 6 publications

References 69 publications

Integration of Multimodal Data Sources for Enhanced Skin Disease Classification and Cancer Prediction: A Study Leveraging Pre-Trained Models on HAM_10000 Metadata and Squamous Cell Carcinoma (SCC) Images.

Integration of Multimodal Data Sources for Enhanced Skin Disease Classification and Cancer Prediction: A Study Leveraging Pre-Trained Models on HAM_10000 Metadata and Squamous Cell Carcinoma (SCC) Images.

Integration of Multimodal Data Sources for Enhanced Skin Disease Classification and Cancer Prediction: A Study Leveraging Pre-Trained Models on HAM_10000 Metadata and Squamous Cell Carcinoma (SCC) Images.

Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects

Contact Info

Product

Resources

About