This research evaluates the performance of the E-VGG19 model for skin cancer detection and classification. The study aims to enhance the accuracy of skin cancer detection using a combination of pre-trained deep learning models and traditional machine learning classifiers. The E-VGG19 model is an enhanced version of the VGG19 pre-trained model, incorporating additional layers and hyperparameters to improve feature extraction and classification accuracy. The study uses a skin lesion dataset containing both malignant and benign cases for training and testing. The models extract features from the images and classify them into two categories: malignant and benign. The features are then fed into machine learning methods such as Linear Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM). The results show that combining the E-VGG19 model with traditional classifiers significantly improves the overall classification accuracy for skin cancer detection and classification. The study also compares the performance of baseline classifiers and pre-trained models using metrics such as recall, F1 score, precision, sensitivity, and accuracy. The experiment results provide valuable insights into the effectiveness of various models and classifiers for accurate and efficient skin cancer detection. The research contributes to the ongoing efforts to create automated technologies for detecting skin cancer that can help healthcare professionals and individuals identify potential skin cancer cases at an early stage, ultimately leading to more timely and effective treatments. The study also explores the use of machine learning techniques for skin cancer detection, including feature extraction, selection, and fusion methods. The results show that the E-VGG19 model outperforms other pre-trained models in terms of accuracy and performance. The study also discusses the use of transfer learning and deep learning techniques for skin cancer detection, including the use of CNNs and other deep learning models. The results show that the E-VGG19 model achieves high accuracy in skin cancer detection and classification, making it a promising approach for automated skin cancer detection. The study also highlights the importance of proper dataset curation and quality assurance for building accurate and reliable melanoma detection models. The results demonstrate that the E-VGG19 model is a promising approach for skin cancer detection and classification, with high accuracy and performance. The study also discusses the use of various machine learning classifiers and deep learning models for skin cancer detection, including the use of SVM, KNN, and logistic regression. The results show that the E-VGG19 model outperforms other models in terms of accuracy and performance. The study also highlights the importance of data augmentation and preprocessing in improving the performance of skin cancer detection models. The results show that the E-VGG19 model achieves high accuracy in skin cancer detection and classification, making it a promising approach for automated skin cancer detection. The study also discusses the use of various machine learning techniques for skin cancer detection, including feature extraction, selection, and fusion methods. The results show that the E-VGG19 model outThis research evaluates the performance of the E-VGG19 model for skin cancer detection and classification. The study aims to enhance the accuracy of skin cancer detection using a combination of pre-trained deep learning models and traditional machine learning classifiers. The E-VGG19 model is an enhanced version of the VGG19 pre-trained model, incorporating additional layers and hyperparameters to improve feature extraction and classification accuracy. The study uses a skin lesion dataset containing both malignant and benign cases for training and testing. The models extract features from the images and classify them into two categories: malignant and benign. The features are then fed into machine learning methods such as Linear Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM). The results show that combining the E-VGG19 model with traditional classifiers significantly improves the overall classification accuracy for skin cancer detection and classification. The study also compares the performance of baseline classifiers and pre-trained models using metrics such as recall, F1 score, precision, sensitivity, and accuracy. The experiment results provide valuable insights into the effectiveness of various models and classifiers for accurate and efficient skin cancer detection. The research contributes to the ongoing efforts to create automated technologies for detecting skin cancer that can help healthcare professionals and individuals identify potential skin cancer cases at an early stage, ultimately leading to more timely and effective treatments. The study also explores the use of machine learning techniques for skin cancer detection, including feature extraction, selection, and fusion methods. The results show that the E-VGG19 model outperforms other pre-trained models in terms of accuracy and performance. The study also discusses the use of transfer learning and deep learning techniques for skin cancer detection, including the use of CNNs and other deep learning models. The results show that the E-VGG19 model achieves high accuracy in skin cancer detection and classification, making it a promising approach for automated skin cancer detection. The study also highlights the importance of proper dataset curation and quality assurance for building accurate and reliable melanoma detection models. The results demonstrate that the E-VGG19 model is a promising approach for skin cancer detection and classification, with high accuracy and performance. The study also discusses the use of various machine learning classifiers and deep learning models for skin cancer detection, including the use of SVM, KNN, and logistic regression. The results show that the E-VGG19 model outperforms other models in terms of accuracy and performance. The study also highlights the importance of data augmentation and preprocessing in improving the performance of skin cancer detection models. The results show that the E-VGG19 model achieves high accuracy in skin cancer detection and classification, making it a promising approach for automated skin cancer detection. The study also discusses the use of various machine learning techniques for skin cancer detection, including feature extraction, selection, and fusion methods. The results show that the E-VGG19 model out