Abstract
Due to the successful application of machine learning techniques in several fields, automated diagnosis system in healthcare has been increasing at a high rate. The aim of the study is to propose an automated skin cancer diagnosis and triaging model and to explore the impact of integrating the clinical features in the diagnosis and enhance the outcomes achieved by the literature study. We used an ensemble‐learning framework, consisting of the EfficientNetB3 deep learning model for skin lesion analysis and Extreme Gradient Boosting (XGB) for clinical data. The study used PAD‐UFES‐20 data set consisting of six unbalanced categories of skin cancer. To overcome the data imbalance, we used data augmentation. Experiments were conducted using skin lesion merely and the combination of skin lesion and clinical data. We found that integration of clinical data with skin lesions enhances automated diagnosis accuracy. Moreover, the proposed model outperformed the results achieved by the previous study for the PAD‐UFES‐20 data set with an accuracy of 0.78, precision of 0.89, recall of 0.86, and F1 of 0.88. In conclusion, the study provides an improved automated diagnosis system to aid the healthcare professional and patients for skin cancer diagnosis and remote triaging.
Highlights
Skin cancer is one of the commonly occurring and deadly types of cancer. e expected estimated number of newly diagnosed skin cancer patients during 2020 in the USA will be more than 1.8 million [1]
Skin cancer is a type of cancer caused by damaged skin cells or abnormal growth of skin cells
It can be mainly categorized as basal cell carcinoma (BCC), melanoma (MEL), nonmelanoma skin cancer, and squamous cell carcinoma (SCC)
Summary
Irfan Ullah Khan ,1 Nida Aslam ,1 Talha Anwar ,2 Sumayh S. E aim of the study is to propose an automated skin cancer diagnosis and triaging model and to explore the impact of integrating the clinical features in the diagnosis and enhance the outcomes achieved by the literature study. E study used PAD-UFES-20 data set consisting of six unbalanced categories of skin cancer. We found that integration of clinical data with skin lesions enhances automated diagnosis accuracy. The proposed model outperformed the results achieved by the previous study for the PAD-UFES-20 data set with an accuracy of 0.78, precision of 0.89, recall of 0.86, and F1 of 0.88. The study provides an improved automated diagnosis system to aid the healthcare professional and patients for skin cancer diagnosis and remote triaging
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