Performance evaluation of E-VGG19 model: Enhancing real-time skin cancer detection and classification.

Skin cancer is a pervasive and potentially life-threatening disease. Early detection plays a crucial role in improving patient outcomes. Machine learning (ML) techniques, particularly when combined with pre-trained deep learning models, have shown promise in enhancing the accuracy of skin cancer detection. In this paper, we enhanced the VGG19 pre-trained model with max pooling and dense layer for the prediction of skin cancer. Moreover, we also explored the pre-trained models such as Visual Geometry Group 19 (VGG19), Residual Network 152 version 2 (ResNet152v2), Inception-Residual Network version 2 (InceptionResNetV2), Dense Convolutional Network 201 (DenseNet201), Residual Network 50 (ResNet50), Inception version 3 (InceptionV3), For training, skin lesions dataset is used with malignant and benign cases. The models extract features and divide skin lesions into two categories: malignant and benign. The features are then fed into machine learning methods, including Linear Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LR) and Support Vector Machine (SVM), our results demonstrate that combining E-VGG19 model with traditional classifiers significantly improves the overall classification accuracy for skin cancer detection and classification. Moreover, we have also compared the performance of baseline classifiers and pre-trained models with metrics (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. This 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.

Impact of Activation, Optimization, and Regularization Methods on the Facial Expression Model Using CNN.

When it comes to conveying sentiments and thoughts, facial expressions are quite effective. For human-computer collaboration, data-driven animation, and communication between humans and robots to be successful, the capacity to recognize emotional states in facial expressions must be developed and implemented. Recently published studies have found that deep learning is becoming increasingly popular in the field of image categorization. As a result, to resolve the problem of facial expression recognition (FER) using convolutional neural networks (CNN), increasingly substantial efforts have been made in recent years. Facial expressions may be acquired from databases like CK+ and JAFFE using this novel FER technique based on activations, optimizations, and regularization parameters. The model recognized emotions such as happiness, sadness, surprise, fear, anger, disgust, and neutrality. The performance of the model was evaluated using a variety of methodologies, including activation, optimization, and regularization, as well as other hyperparameters, as detailed in this study. In experiments, the FER technique may be used to recognize emotions with an Adam, Softmax, and Dropout Ratio of 0.1 to 0.2 when combined with other techniques. It also outperforms current FER techniques that rely on handcrafted features and only one channel, as well as has superior network performance compared to the present state-of-the-art techniques.