Skin cancer is the most dominant and critical method of cancer, which arises all over the world. Its damaging effects can range from disfigurement to major medical expenditures and even death if not analyzed and preserved timely. Conventional models of skin cancer recognition require a complete physical examination by a specialist, which is time-wasting in a few cases. Computer-aided medicinal analytical methods have gained massive popularity due to their efficiency and effectiveness. This model can assist dermatologists in the initial recognition of skin cancer, which is significant for early diagnosis. An automatic classification model utilizing deep learning (DL) can help doctors perceive the kind of skin lesion and improve the patient’s health. The classification of skin cancer is one of the hot topics in the research field, along with the development of DL structure. This manuscript designs and develops a Detection of Skin Cancer Using an Ensemble Deep Learning Model and Gray Wolf Optimization (DSC-EDLMGWO) method. The proposed DSC-EDLMGWO model relies on the recognition and classification of skin cancer in biomedical imaging. The presented DSC-EDLMGWO model initially involves the image preprocessing stage at two levels: contract enhancement using the CLAHE method and noise removal using the wiener filter (WF) model. Furthermore, the proposed DSC-EDLMGWO model utilizes the SE-DenseNet method, which is the fusion of the squeeze-and-excitation (SE) module and DenseNet to extract features. For the classification process, the ensemble of DL models, namely the long short-term memory (LSTM) technique, extreme learning machine (ELM) model, and stacked sparse denoising autoencoder (SSDA) method, is employed. Finally, the gray wolf optimization (GWO) method optimally adjusts the ensemble DL models’ hyperparameter values, resulting in more excellent classification performance. The effectiveness of the DSC-EDLMGWO approach is evaluated using a benchmark image database, with outcomes measured across various performance metrics. The experimental validation of the DSC-EDLMGWO approach portrayed a superior accuracy value of 98.38% and 98.17% under HAM10000 and ISIC datasets across other techniques.

Disease detection is a critical issue in the field of medical diagnostics. Failure to identify heart disease (HD) at an early stage can lead to mortality. The lack of access to expert physicians in remote areas further exacerbates the problem. Big data analytics (BDA) is an emerging area that can help extract valuable information from vast amounts of data and improve medical diagnosis while reducing costs. Machine learning (ML) algorithms have been effectively employed in many fields, including medical diagnostics. ML methods can help doctors detect and forecast illnesses at an early stage by creating classifier systems. In this article, we propose a unique ML- and BDA-based squirrel search-optimized Gradient Boosted Decision Tree (SS-GBDT) for the detection of heart disease. The effectiveness of the proposed method is demonstrated through various performance indicators. The results show that the proposed method is highly efficient in medical diagnosis, with 95% accuracy rate, 95.8% precision, 96.8% recall and 96.3% F1-measure achieved by the SS-GBDT. The use of BDA and ML can greatly enhance medical diagnosis and this proposed method is a significant step in this direction.

Breast cancer poses the greatest long-term health risk to women worldwide, in both industrialized and developing nations. Early detection of breast cancer allows for treatment to begin before the disease has a chance to spread to other parts of the body. The Internet of Things (IoT) allows for automated analysis and classification of medical pictures, allowing for quicker and more effective data processing. Nevertheless, Fog computing principles should be used instead of Cloud computing concepts alone to provide rapid responses while still meeting the requirements for low latency, energy consumption, security, and privacy. In this paper, we present CanDiag, an approach to cancer diagnosis based on Transfer Deep Learning (TDL) that makes use of Fog computing. This paper details an automated, real-time approach to diagnosing breast cancer using deep learning (DL) and mammography pictures from the Mammographic Image Analysis Society (MIAS) library. To obtain better prediction results, transfer learning (TL) techniques such as GoogleNet, ResNet50, ResNet101, InceptionV3, AlexNet, VGG16, and VGG19 were combined with the well-known DL approach of the convolutional neural network (CNN). The feature reduction technique principal component analysis (PCA) and the classifier support vector machine (SVM) were also applied with these TDLs. Detailed simulations were run to assess seven performance and seven network metrics to prove the viability of the proposed approach. This study on an enormous dataset of mammography images categorized as normal and abnormal, respectively, achieved an accuracy, MCR, precision, sensitivity, specificity, f1-score, and MCC of 99.01%, 0.99%, 98.89%, 99.86%, 95.85%, 99.37%, and 97.02%, outperforming some previous studies based on mammography images. It can be shown from the trials that the inclusion of the Fog computing concepts empowers the system by reducing the load on centralized servers, increasing productivity, and maintaining the security and integrity of patient data.

Computer vision heavily relies on features, especially in image classification tasks using feature-based architectures. Dimensionality reduction techniques are employed to enhance computational performance by reducing the dimensionality of inner layers. Convolutional Neural Networks (CNNs), originally designed to recognize critical image components, now learn features across multiple layers. Bidirectional LSTM (BiLSTM) networks store data in both forward and backward directions, while traditional Long Short-Term Memory (LSTM) networks handle data in a specific order. This study proposes a computer vision system that integrates BiLSTM with CNN features for image categorization tasks. The system effectively reduces feature dimensionality using learned features, addressing the high dimensionality problem in leaf image data and enabling early, accurate disease identification. Utilizing CNNs for feature extraction and BiLSTM networks for temporal dependency capture, the method incorporates label information as constraints, leading to more discriminative features for disease classification. Tested on datasets of pepper and maize leaf images, the method achieved a 99.37% classification accuracy, outperforming existing dimensionality reduction techniques. This cost-effective approach can be integrated into precision agriculture systems, facilitating automated disease detection and monitoring, thereby enhancing crop yields and promoting sustainable farming practices. The proposed Efficient Labelled Feature Dimensionality Reduction utilizing CNN-BiLSTM (ELFDR-LDC-CNN-BiLSTM) model is compared to current models to show its effectiveness in reducing extracted features for leaf detection and classification tasks.

Arrhythmias are a leading cause of cardiovascular morbidity and mortality. Portable electrocardiogram (ECG) monitors have been used for decades to monitor patients with arrhythmias. These monitors provide real-time data on cardiac activity to identify irregular heartbeats. However, rhythm monitoring and wave detection, especially in the 12-lead ECG, make it difficult to interpret the ECG analysis by correlating it with the condition of the patient. Moreover, even experienced practitioners find ECG analysis challenging. All of this is due to the noise in ECG readings and the frequencies at which the noise occurs. The primary objective of this research is to remove noise and extract features from ECG signals using the proposed infinite impulse response (IIR) filter to improve ECG quality, which can be better understood by non-experts. For this purpose, this study used ECG signal data from the Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) database. This allows the acquired data to be easily evaluated using machine learning (ML) and deep learning (DL) models and classified as rhythms. To achieve accurate results, we applied hyperparameter (HP)-tuning for ML classifiers and fine-tuning (FT) for DL models. This study also examined the categorization of arrhythmias using different filters and the changes in accuracy. As a result, when all models were evaluated, DenseNet-121 without FT achieved 99% accuracy, while FT showed better results with 99.97% accuracy.