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There are many different kinds of skin cancer, and an early and precise diagnosis is crucial because skin cancer is both frequent and deadly. The key to effective treatment is accurately classifying the various skin cancers, which have unique traits. Dermoscopy and other advanced imaging techniques have enhanced early detection by providing detailed images of lesions. However, accurately interpreting these images to distinguish between benign and malignant tumors remains a difficult task. Improved predictive modeling techniques are necessary due to the frequent occurrence of erroneous and inconsistent outcomes in the present diagnostic processes. Machine learning (ML) models have become essential in the field of dermatology for the automated identification and categorization of skin cancer lesions using image data. The aim of this work is to develop improved skin cancer predictions by using ensemble models, which combine numerous machine learning approaches to maximize their combined strengths and reduce their individual shortcomings. This paper proposes a fresh and special approach for ensemble model optimization for skin cancer classification: the Max Voting method. We trained and assessed five different ensemble models using the ISIC 2018 and HAM10000 datasets: AdaBoost, CatBoost, Random Forest, Gradient Boosting, and Extra Trees. Their combined predictions enhance the overall performance with the Max Voting method. Moreover, the ensemble models were fed with feature vectors that were optimally generated from the image data by a genetic algorithm (GA). We show that, with an accuracy of 95.80%, the Max Voting approach significantly improves the predictive performance when compared to the five ensemble models individually. Obtaining the best results for F1-measure, recall, and precision, the Max Voting method turned out to be the most dependable and robust. The novel aspect of this work is that skin cancer lesions are more robustly and reliably classified using the Max Voting technique. Several pre-trained machine learning models’ benefits are combined in this approach.

Skin cancer is common and deadly, hence a correct diagnosis at an early age is essential. Effective therapy depends on precise classification of the several skin cancer forms, each with special traits. Because dermoscopy and other sophisticated imaging methods produce detailed lesion images, early detection has been enhanced. It’s still difficult to analyze the images to differentiate benign from malignant tumors, though. Better predictive modeling methods are needed since the diagnostic procedures used now frequently produce inaccurate and inconsistent results. In dermatology, Machine learning (ML) models are becoming essential for the automatic detection and classification of skin cancer lesions from image data. With the ensemble model, which mix several ML approaches to take use of their advantages and lessen their disadvantages, this work seeks to improve skin cancer predictions. We introduce a new method, the Max Voting method, for optimization of skin cancer classification. On the HAM10000 and ISIC 2018 datasets, we trained and assessed three distinct ML models: Random Forest (RF), Multi-layer Perceptron Neural Network (MLPN), and Support Vector Machine (SVM). Overall performance was increased by the combined predictions made with the Max Voting technique. Moreover, feature vectors that were optimally produced from image data by a Genetic Algorithm (GA) were given to the ML models. We demonstrate that the Max Voting method greatly improves predictive performance, reaching an accuracy of 94.70% and producing the best results for F1-measure, recall, and precision. The most dependable and robust approach turned out to be Max Voting, which combines the benefits of numerous pre-trained ML models to provide a new and efficient method for classifying skin cancer lesions.

Human metapneumovirus (hMPV) is a significant cause of respiratory illness, particularly in children, elderly individuals, and immunocompromised patients. Despite its clinical relevance, hMPV poses diagnostic challenges due to its symptom similarity with other respiratory illnesses, such as influenza and respiratory syncytial virus (RSV), and the lack of specialized detection systems. Traditional diagnostic methods are often inadequate for providing rapid and accurate results, particularly in low-resource settings. This study proposes a novel deep learning framework, referred to as hMPV-Net, which leverages Convolutional Neural Networks (CNNs) to facilitate the precise detection and classification of hMPV infections. The CNN model is designed to perform binary classification by differentiating between hMPV-positive and hMPV-negative cases. To address the lack of real-world patient data, simulated image datasets were used for model training and evaluation, allowing the model to generalize to various clinical scenarios. A key challenge in developing this model is the imbalance within the dataset, where hMPV-positive cases are often underrepresented. To mitigate this, the framework incorporates advanced techniques such as data augmentation, weighted loss functions, and dropout regularization, which help to balance the dataset, improve model robustness, and enhance classification accuracy. These techniques are crucial in addressing issues such as overfitting and generalization, which are common when working with limited datasets in medical imaging tasks. The dataset used for model training and testing consists of 10,000 samples, with an equal distribution of hMPV-positive and hMPV-negative cases. Experimental results demonstrate that the hMPV-Net model achieves a high test accuracy of 91.8%, along with impressive test precision, recall, and F1-score values around 92%. These metrics indicate that the model performs exceptionally well in classifying both hMPV-positive and hMPV-negative cases. Furthermore, the model exhibits superior computational efficiency, requiring only 3.2 GFLOPs, which is significantly lower than other state-of-the-art models such as ResNet-50 and VGG-16. This reduction in computational cost makes the model suitable for deployment in resource-constrained healthcare environments, where computing power and infrastructure may be limited.