An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset.

Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today's medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient's death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure. The efficient detection of skin cancer is a challenging task. Therefore, the numbers of skilful dermatologists around the globe are not enough to deal with today's healthcare. The huge difference between data from various healthcare sector classes leads to data imbalance problems. Due to data imbalance issues, deep learning models are often trained on one class more than others. This study proposes a novel deep learning-based skin cancer detector using an imbalanced dataset. Data augmentation was used to balance various skin cancer classes to overcome the data imbalance. The Skin Cancer MNIST: HAM10000 dataset was employed, which consists of seven classes of skin lesions. Deep learning models are widely used in disease diagnosis through images. Deep learning-based models (AlexNet, InceptionV3, and RegNetY-320) were employed to classify skin cancer. The proposed framework was also tuned with various combinations of hyperparameters. The results show that RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets. The performance of the proposed framework was better than that of conventional methods. The accuracy, F1-score, and ROC curve value obtained with the proposed framework were 91%, 88.1%, and 0.95, which were significantly better than those of the state-of-the-art method, which achieved 85%, 69.3%, and 0.90, respectively. Our proposed framework may assist in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare professionals.

Exer-gaming reduces fall risk and improves mobility after stroke.

The current study evaluated the effect of virtual reality based balance training in 30 stroke patients recruited via purposive sampling technique for a clinical trial. Sealed envelope method was used to randomly allocate patients into two groups, i.e. Exer-gaming group (EGG) (n=15) and traditional training (TBT) group (n=15). Patients ranging in age from 50 to 60 years were included using Modified Rankin Scale (MRS). Patients with cognitive deficits, severe physical impairments, contractures, inability to perform tasks, complications of the joint that affected movement, history of recent fracture, arthritis and those on drugs that could affect their physical function were excluded. Data was collected using Berg Balance Scale (BBS) and Timed Up & Go Test (TUG). Significant improvement was observed in the exer-gaming training group after completing intervention (P<0.001). Exer-gaming appears to be more effective in improving functional level, mobility and balance in stroke patients. The study also suggests that exer-gaming further provides dynamic environment for stroke patients, thereby improving dynamic balance and mobility.