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The rising number of cases of human Mpox has emerged as a major global concern due to the daily increase of cases in several countries. The disease presents various skin symptoms in infected individuals, making it crucial to promptly identify and isolate them to prevent widespread community transmission. Rapid determination and isolation of infected individuals are therefore essential to curb the spread of the disease. Most research in the detection of Mpox disease has utilized convolutional neural network (CNN) models and ensemble methods. However, to the best of our knowledge, none have utilized a meta-heuristic-based ensemble approach. To address this gap, we propose a novel metaheuristics optimization-based weighted average ensemble model (MO-WAE) for detecting Mpox disease. We first train three transfer learning (TL)-based CNNs (DenseNet201, MobileNet, and DenseNet169) by adding additional layers to improve their classification strength. Next, we use a weighted average ensemble technique to fuse the predictions from each individual model, and the particle swarm optimization (PSO) algorithm is utilized to assign optimized weights to each model during the ensembling process. By using this approach, we obtain more accurate predictions than individual models. To gain a better understanding of the regions indicating the onset of Mpox, we performed a Gradient Class Activation Mapping (Grad-CAM) analysis to explain our model's predictions. Our proposed MO-WAE ensemble model was evaluated on a publicly available Mpox dataset and achieved an impressive accuracy of 97.78%. This outperforms state-of-the-art (SOTA) methods on the same dataset, thereby providing further evidence of the efficacy of our proposed model. • A weighted ensemble model utilizing meta-heuristic optimization for Mpox detection. • PSO is utilized to optimize the weights assigned to each base model. • Utilizing pre-trained CNNs for deep feature extraction via transfer learning. • The new weight assignment method leads to an improvement in the overall performance. • Proposed model shows better performance than current state-of-the-art. [ABSTRACT FROM AUTHOR]

The rising prevalence of oral and dental conditions, including issues like gum disease and oral cancer, presents a pressing global health challenge. The promptly identification of individuals at risk of developing these potentially life-threatening oral ailments, especially those linked to lip and oral cavity cancer. Early detection is crucial for effective intervention and containment of these diseases. Several studies in medical research have explored the use of convolutional neural network (CNN) models with pre-trained weights to identify oral and dental diseases. Previous research has primarily focused on amalgamating predictions from various models to boost accuracy, the integration of diverse model predictions presents challenges such as potential inconsistencies, conflicting outcomes, and heightened model complexity. However, utilizing a variety of model features can not only boost the robustness and accuracy of the detection process but also streamline the overall complexity of the model ensemble. The challenge lies in finding more effective methods to combine and leverage features extracted from deep learning models for superior predictive accuracy. To tackle this challenge, our research introduces an innovative approach that incorporates feature-level fusion techniques for oral cancer detection. We have developed a novel self-attention block as a foundational element of our approach. Additionally, we utilize transfer learning (TL) models for feature fusion, specifically EfficientNetB0 and EfficientNetB1, renowned for their effectiveness in image classification tasks. During the training process, we incorporate a self-attention block, a crucial innovation that facilitates the fusion of features extracted from these EfficientNet models. Our proposed methodology revolves around the concept of feature-level fusion. We employ a novel self-attention block and leverage transfer learning models to extract and combine relevant features. This fusion strategy enables us to collective strengths of these models, resulting in more accurate predictions than what can be achieved using each model in isolation. To evaluate the effectiveness of our approach, we conducted rigorous assessments using the well-established MOD dataset, which is a publicly available benchmark in the field of oral cancer detection. Our model achieved an impressive accuracy rate of 98.83% in these evaluations. [ABSTRACT FROM AUTHOR]

• Our ensemble combines five transfer learning-based models to enhance disease detection capabilities. • We enhance the base models by incorporating feature integration layers and residual blocks, allowing them to extract intricate features and patterns effectively. • We employ the chaos game optimization algorithm to optimize base model weights efficiently, resulting in improved ensemble performance. • Our evaluation, conducted on both benchmark and newly curated datasets, highlights CGO's superiority in terms of accuracy and ensemble effectiveness. • Our approach presents a robust solution for the detection of monkeypox from skin images, significantly enhancing accuracy in disease diagnosis. The rising global incidence of human Mpox cases necessitates prompt and accurate identification for effective disease control. Previous studies have predominantly delved into traditional ensemble methods for detection, we introduce a novel approach by leveraging a metaheuristic-based ensemble framework. In this research, we present an innovative CGO-Ensemble framework designed to elevate the accuracy of detecting Mpox infection in patients. Initially, we employ five transfer learning base models that integrate feature integration layers and residual blocks. These components play a crucial role in capturing significant features from the skin images, thereby enhancing the models' efficacy. In the next step, we employ a weighted averaging scheme to consolidate predictions generated by distinct models. To achieve the optimal allocation of weights for each base model in the ensemble process, we leverage the Chaos Game Optimization (CGO) algorithm. This strategic weight assignment enhances classification outcomes considerably, surpassing the performance of randomly assigned weights. Implementing this approach yields notably enhanced prediction accuracy compared to using individual models. We evaluate the effectiveness of our proposed approach through comprehensive experiments conducted on two widely recognized benchmark datasets: the Mpox Skin Lesion Dataset (MSLD) and the Mpox Skin Image Dataset (MSID). To gain insights into the decision-making process of the base models, we have performed Gradient Class Activation Mapping (Grad-CAM) analysis. The experimental results showcase the outstanding performance of the CGO-ensemble, achieving an impressive accuracy of 100% on MSLD and 94.16% on MSID. Our approach significantly outperforms other state-of-the-art optimization algorithms, traditional ensemble methods, and existing techniques in the context of Mpox detection on these datasets. These findings underscore the effectiveness and superiority of the CGO-Ensemble in accurately identifying Mpox cases, highlighting its potential in disease detection and classification. [ABSTRACT FROM AUTHOR]

• We propose DCDS-Net based on depth-wise separable convolutional neural network. • Data augmentation is used to improve performance and reduce overfitting. • We have analyzed the effect and significance of block wise fine-tuning strategy. • Extensive experiments and detailed comparative analysis were presented. • The proposed model shows excellent classification performance even on small dataset. Gastrointestinal (GI) diseases are the most common in the human digestive system and has a significantly higher mortality rate. Accurate evaluation of endoscopic images plays an important role in decision making regarding patient treatment. Recently, convolutional neural networks (CNNs) have been introduced for the diagnosis of GI diseases. However, achieving high accuracy is still a challenging task. To overcome these limitations, we propose the "Densely Connected Depth-wise Separable Convolution-Based Network" (DCDS-Net) model, utilizing depth-wise separable convolution (DWSC) with residual connections and densely connected blocks (DCB), to effectively diagnose various endoscopic images of GI diseases. In addition, we incorporate global average pooling (GAP), batch normalization, dropout and dense layers in DCB to learn rich discriminative features and improve the performance of the model. We explored the feasibility of block-wise fine-tuning using transfer learning on the proposed model to reduce overfitting, and experimentally explore the optimal level of fine-tuning, since transfer learning is well suited to medical data where labeled data is scarce. The proposed method has been evaluated on 6000 labeled endoscopic images containing 4 classes of GI diseases. In addition, data augmentation has been incorporated into the training pipeline to improve the performance of the model. Furthermore, a critical study was conducted to evaluate the generalizability of the proposed model on smaller training samples (e.g., 60 %, 70 %, 80 %, and 90 %). The study employed Grad-CAM to generate heatmaps that identify the regions in the GI tract that are indicative of the presence of different diseases. The results of extensive experiments show that the proposed model shows significant improvements and achieves the highest classification accuracy of 99.33 %, precision of 99.37 %, recall of 99.32 % and outperforms all pre-trained and existing models for the detection of GI diseases. In conclusion, DCDS-Net exhibits high classification performance and can help endoscopists in automatic GI disease diagnosis. [ABSTRACT FROM AUTHOR]