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Bone cancer detection is an essential region of medical analysis but developments in medical imaging and artificial intelligence (AI) are vital. Using approaches, namely deep learning (DL) and machine learning (ML), radiologists and medical staff can examine X-ray, CT, and MRI scans to identify bone cancer and abnormalities. These technologies support earlier diagnosis, correct diagnosis, and treatment planning, enhancing patient solutions. The combination of AI-driven image analysis and the knowledge of medical practitioners improves the speed and precision of bone cancer detection, contributing to more effectual clinical activities. DL algorithms, particularly CNNs, are exposed to great performance in image classification tasks and are extremely utilized for medical image analysis. We offer a Hybrid Rice Optimization Algorithm with DL-Assisted Bone Cancer Detection (HROADL-BCD) technique on medical X-ray images. The major intention of the HROADL-BCD method is to examine the X-ray images for the recognition of bone cancer. In the presented HROADL-BCD method, a bilateral filtering (BF) process was performed to remove the noise. To derive feature vectors, the HROADL-BCD technique applied the EfficientNet model. The HROADL-BCD technique involved the HROA for hyperparameter tuning of the EfficientNet model. Last, the bone cancer detection and classification process were executed by the attention-based bidirectional long short-term memory (ABiLSTM) approach. A wide range of simulations could be applied for the simulation result analysis of the HROADL-BCD algorithm. The extensive outcome of the HROADL-BCD approach inferred the superior outcome of 97.62% outcome concerning various aspects.

Chronic diseases, such as diabetes, cause serious challenges worldwide due to their long-lasting effect on health and quality of life. Diabetes, considered a high glucose level, requires continuous management to avoid complications like kidney failure, low vision, and heart disease. Leveraging state-of-the-art technology, especially IoT devices, shows great potential for optimizing the recognition and management of diabetes. These devices, including smart glucose monitors and wearable sensors, provide real-time data on crucial health metrics, enabling early intervention and proactive monitoring. Furthermore, incorporating deep learning (DL) techniques improves data analysis and recognizes risk factors and subtle patterns related to diabetes. By integrating IoT technology with DL techniques, the healthcare system can empower patients with tools for self-management and develop more conventional approaches for earlier diagnosis and personalized treatment plans, ultimately improving long-term health outcomes and reducing the burden of diabetes. This study develops a new IoT-enabled Driven Early Detection of Chronic Disease using the DL and Dimensionality Reduction (EDCD-DLDR) approach. The EDCD-DLDR method aims to enable IoT devices to collect patient medical data and employ the DL model for earlier diagnosis of diabetes. In the EDCD-DLDR technique, the IoT-based data acquisition process is initially involved, and the collected data gets normalized using Z-score normalization. The EDCD-DLDR technique uses an artificial rabbit optimizer-based feature selection (ARO-FS) approach for dimensionality reduction. In addition, the detection of diabetes is achieved by the attention bidirectional gated recurrent unit (ABiGRU) model. The pelican optimization algorithm (POA) based hyperparameter selection model is included to improve the performance of the ABiGRU network. A comprehensive set of simulations is made to highlight the performance of the EDCD-DLDR method on the Kaggle dataset. The experimental validation of the EDCD-DLDR method underscored a superior accuracy value of 97.14% over existing techniques.

Gastrointestinal or gastric cancer (GC) classification is a serious field of medical research and healthcare technology, where innovative machine learning (ML) and deep learning (DL) models are employed to categorize and analyze many kinds of GCs like pancreatic, gastric, or colorectal cancer. These models influence features extracted from medical imaging, genetic, and clinical data to distinguish between benign and malignant tumours, define cancer stages, and guide treatment verdicts. By automating cancer detection and classification procedures, DL techniques help healthcare experts make quicker and more exact analyses, leading to superior patient results, modified treatment tactics, and enhanced complete organization of GC cases. This technique has great promise for transforming initial detection and involvement in the battle against dangerous diseases. With this inspiration, this study presents a new snake optimization algorithm with a DL-assisted GC classification (SOADL-GCC) approach. The SOADL-GCC approach aims to examine the gastrointestinal tract images for the detection and classification of GC. To achieve it, the SOADL-GCC model employs a bilateral filtering (BF) approach for the noise removal process and enhances image quality. Besides, the SOADL-GCC technique uses a capsule network (CapsNet) model for deriving the feature vectors from preprocessed images. Moreover, SOA can achieve the optimum assortment of hyperparameters associated with the CapsNet model. Finally, the classification process can be performed using the deep belief network (DBN) model. A sequence of simulations took place on the Kvasir dataset to evaluate improved detection results of SOADL-GCC technology. An extensive comparative study reported that the SOADL-GCC technique effectively performs well with other models with a maximum accuracy of 99.72%.