P. Muthu

For many years, wheelchairs have been an invaluable asset to disabled and diseased individuals with limited mobility. From the early seventeenth century to today, the wheelchair remains a prevalent tool in the daily lives of humans. It is used widely, in hospitals, care facilities, homes and other public areas as well. From the very first wheelchair design in the late sixteenth century, this device has been subjected to multiple iterations, all aimed at improving the user’s comfort and the chair’s usability. In the modern world, there are numerous different types of wheelchairs - manual, motorized, for indoor use, for outdoor use and the list goes on. Through research and multiple observations, it is noted that traditional motorized wheelchairs, though successful in restoring movement and a sense of normality in patients, still face limitations in adapting to diverse user needs, especially in the twenty-first century. This work, hence, aims to introduce a ground breaking smart voice-controlled wheelchair with a re-assemblable framework designed to elevate functionality and user independence. This solution proposes a unique re-assemblable framework to address the dire challenges of portability that also allows users to modify the wheelchair's configuration to suit their requirements. The cutting-edge voice recognition technologies of a patient’s smartphone are seamlessly integrated into the device to enable hands-free control over movement, which also supports a wider spectrum of patients to self-operate the chair. Finally, the chair is also equipped with obstacle detection sensors for real-time navigation assistance that aims to prioritize user safety. The simple and user-friendly operation of this chair enhances personalization, aiming to redefine mobility and subsequently empower individuals with physical disabilities.

Chronic venous insufficiency (CVI) is a venous disorder characterized by impaired blood flow from the lower extremities back to the heart, leading to various symptoms and complications. Accurate and timely diagnosis of CVI is critical for effective management and prevention of further complications. Deep learning (DL) models have shown results that are promising in the field of medical image analysis tasks over the past few years. This article presents CVINet, a enhanced DL‐based convolutional neural network (CNN) model that has been developed specifically for the purpose of diagnosing CVI utilizing thermal imaging data. The proposed CNN model extracts relevant features from input images using a multi‐layered architecture with convolutional layers and pooling layers. Following feature extraction, data are directed to fully connected layers for performing classification task. To enhance the performance of this model, we employ techniques such as dropout regularization and batch normalization. This model utilizes its ability to automatically learn discriminative features from raw thermal images. A large set of thermal images from both CVI patients and healthy individuals was acquired for training and testing of the model. A comparison between few conventional pre‐trained models was conducted, and the best performing model for classification was examined. Pre‐trained VGG‐16, EfficientNet‐B0, and ResNet‐152 models produced classification accuracies of 94.7%, 95.3%, and 95.8%, respectively. The Proposed CVINet model attained a classification accuracy of 96.8% on binary classification, demonstrating high performance compared with other state‐of‐the‐art networks. Overall, our proposed CNN model offers a promising solution for accurately classifying CVI and normal subjects based on thermal images which outperforms the diagnosis by clinician. The potential benefits include early detection of CVI and personalized treatment strategies that can improve patient outcomes. They improve the accuracy of patient diagnosis, reducing radiologist workload and providing them with a tool that can automatically assess the condition of the lower limb venous condition, lowering the risk of misdiagnosis. Furthermore, improving healthcare service quality and early detection can alter the course of the disease and reduce the morbidity and mortality rate of the disease.

Chronic Venous Insufficiency (CVI) is a venous incompetence condition that leads to improper blood circulation from the lower limbs towards the heart. This occurs as a result of blood pooling in the veins of the leg, resulting in twisted, dilated, and tortuous veins. Aging, obesity, prolonged standing or sitting, and lack of mobility are all important causes of the occurrence of this chronic disease. The cost of CVI diagnosis and treatment is extremely high. Infrared thermographic image analysis is used for early detection and reduces the cost of diagnosis. Deep learning (DL) techniques play an important role in early prediction and may aid clinicians in diagnosing CVI. An automated classification model will assist the physician in making a precise diagnosis of the abnormal vein and treating the patient according to the severity of the condition. There is a need for a model that can perform successful classification without the need for pre-processing when compared to the traditional machine learning (ML) methods that depend on ideal manual feature extraction to achieve optimal outcomes. In this research, we recommend the customized DenseNet-121 architecture for CVI detection and compare it with other advanced DL models to determine its efficacy. DenseNet-121 and other pre-trained convolutional neural network models, including EfficientNetB0 and Inception_v3, were trained using a transfer learning strategy. The experimental findings indicate that the proposed modified DenseNet-121 model outperformed other classical methods. The reported results provide evidence of the robustness of the suggested method in addition to the high accuracy that it possessed, as shown by the overall testing accuracy of 97.4%. Thus, this study can be considered as a non-invasive and cost-effective approach for diagnosing chronic venous insufficiency condition in lower extremity.

Plaque deposits in the carotid artery are the major cause of stroke and atherosclerosis. Ultrasound imaging is used as an early indicator of disease progression. Classification of the images to identify plaque presence and intima-media thickness (IMT) by machine learning algorithms requires features extracted from the images. A total of 361 images were used for feature extraction, which will assist in further classification of the carotid artery. This study presents the extraction of 65 features, which constitute of shape, texture, histogram, correlogram, and morphology features. Principal component analysis (PCA)-based feature selection is performed, and the 22 most significant features, which will improve the classification accuracy, are selected. Naive Bayes algorithm and dynamic learning vector quantization (DLVQ)-based machine learning classifications are performed with the extracted and selected features, and analysis is performed.

Atherosclerotic plaque deposit in the carotid artery is used as an early estimate to identify the presence of cardiovascular diseases. Ultrasound images of the carotid artery are used to provide the extent of stenosis by examining the intima-media thickness and plaque diameter. A total of 361 images were classified using machine learning and deep learning approaches to recognize whether the person is symptomatic or asymptomatic. CART decision tree, random forest, and logistic regression machine learning algorithms, convolutional neural network (CNN), Mobilenet, and Capsulenet deep learning algorithms were applied in 202 normal images and 159 images with carotid plaque. Random forest provided a competitive accuracy of 91.41% and Capsulenet transfer learning approach gave 96.7% accuracy in classifying the carotid artery ultrasound image database.