The word radiomics, like all domains of type omics, assumes the existence of a large amount of data. Using artificial intelligence, in particular, different machine learning techniques, is a necessary step for better data exploitation. Classically, researchers in this field of radiomics have used conventional machine learning techniques (random forest, for example). More recently, deep learning, a subdomain of machine learning, has emerged. Its applications are increasing, and the results obtained so far have demonstrated their remarkable effectiveness. Several previous studies have explored the potential applications of radiomics in colorectal cancer. These potential applications can be grouped into several categories like evaluation of the reproducibility of texture data, prediction of response to treatment, prediction of the occurrence of metastases, and prediction of survival. Few studies, however, have explored the potential of radiomics in predicting recurrence-free survival. In this study, we evaluated and compared six conventional learning models and a deep learning model, based on MRI textural analysis of patients with locally advanced rectal tumours, correlated with the risk of recidivism; in traditional learning, we compared 2D image analysis models vs. 3D image analysis models, models based on a textural analysis of the tumour versus models taking into account the peritumoural environment in addition to the tumour itself. In deep learning, we built a 16-layer convolutional neural network model, driven by a 2D MRI image database comprising both the native images and the bounding box corresponding to each image.

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To avoid dire situations, the medical sector must develop various methods for quickly and accurately identifying infections in remote regions. The primary goal of the proposed work is to create a wearable device that uses the Internet of Things (IoT) to carry out several monitoring tasks. To decrease the amount of communication loss as well as the amount of time required to wait before detection and improve detection quality, the designed wearable device is also operated with a multi-objective framework. Additionally, a design method for wearable IoT devices is established, utilizing distinct mathematical approaches to solve these objectives. As a result, the monitored parametric values are saved in a different IoT application platform. Since the proposed study focuses on a multi-objective framework, state design and deep learning (DL) optimization techniques are combined, reducing the complexity of detection in wearable technology. Wearable devices with IoT processes have even been included in current methods. However, a solution cannot be duplicated using mathematical approaches and optimization strategies. Therefore, developed wearable gadgets can be applied to real-time medical applications for fast remote monitoring of an individual. Additionally, the proposed technique is tested in real-time, and an IoT simulation tool is utilized to track the compared experimental results under five different situations. In all of the case studies that were examined, the planned method performs better than the current state-of-the-art methods.

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