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Currently, skin cancer is a very dangerous disease for humans. Skin cancer is classified into many types such as Melanoma, Basal and Squamous cell carcinoma. In all types of cancer, melanoma is the most dangerous and unpredictable disease. Detection of melanoma cancer at an early stage is useful for effective treatment and can be used to classify t...
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Context 1
... obtaining the training graph, the model was then evaluated using a confusion matrix and a classification report, as shown in Figures 5 and 6. Figure 5 represents the confusion matrix table, which shows the number of true positive (TP), false positive (FP), false negative (FN), and true negative (TN) predictions made by the model for each class. ...
Context 2
... obtaining the training graph, the model was then evaluated using a confusion matrix and a classification report, as shown in Figures 5 and 6. Figure 5 represents the confusion matrix table, which shows the number of true positive (TP), false positive (FP), false negative (FN), and true negative (TN) predictions made by the model for each class. From the confusion matrix table, it can be seen that the model accurately predicted 33 data points for class 0 (melanoma) and incorrectly predicted some data points for class 0. Similarly, the model accurately predicted 6 data points for class 1 (nevus) and incorrectly predicted 6 data points for class 2 (Seborrheic_keratosis). ...
Context 3
... the confusion matrix table, it can be seen that the model accurately predicted 33 data points for class 0 (melanoma) and incorrectly predicted some data points for class 0. Similarly, the model accurately predicted 6 data points for class 1 (nevus) and incorrectly predicted 6 data points for class 2 (Seborrheic_keratosis). In Figure 5, the evaluation of the model using classification_report is presented. The results show that the model achieved an accuracy of 88%, with the highest precision and recall values obtained for class 0 (melanoma), which were 0.85 and 1.00, respectively. ...
Citations
Artificial intelligence uses advanced algorithms such as deep learning and machine learning methods to help doctors make more accurate diagnoses, identify potential health risks, and customize personalized treatment plans for patients. This literature review explores machine learning and deep learning methods applied to medical datasets over the past five years. The paper discusses the advancements, challenges, and future directions in utilizing ML and DL techniques for medical data analysis. It synthesizes recent research findings, highlighting key methodologies, datasets, and outcomes.
