In this work, a novel skin lesion detection approach, called HBCENCM, is proposed using histogram-based clustering estimation (HBCE) algorithm to determine the required number of clusters in the neutrosophic c-means clustering (NCM) method. Initially, the dermoscopic images are mapped into the neutrosophic domain over three memberships, namely true, indeterminate, and false subsets. Then, an NCM algorithm is employed to group the pixels in the dermoscopy images, where the number of clusters in the dermoscopy images is determined using the HBCE algorithm. Lastly, the skin lesion is detected based on its intensity and morphological features. The public dataset (ISIC 2016) of 900 images for training and 379 images for testing are used in the present work. A comparative study of the original NCM clustering method is conducted on the same dataset. The results showed the superiority of the proposed approach to detect the lesion with 96.3% average accuracy compared to the average accuracy of 94.6% using the original NCM without HBCE algorithm.

No abstract

In this study, MR Image segmentation has been realized with some clustering algorithms. In the study, the performances kmeans, lloyds, llyds-kmeans, pso clustering, ga clustering and jaya optimisation algorithms on some MR images from BRATS 2012 dataset have been compared. For the comparison, the manual segmentation results of MR images from BRATS 2012 dataset have been referenced and results have been compared with these referances. In the comparison RI (Rand Index), VOI (Variation of Information) and GCE (Global Consistency Error) have been used and results have been submitted. The results showed that the PSO algorithm yielded better results and has a better processing time than the other algorithms.

The COVID-19 pandemic has caused millions of deaths and changed daily life globally. Countries have declared a half or full lockdown to prevent the spread of COVID-19. According to medical doctors, as many people as possible should be tested to identify their status, and corresponding actions then should be taken for COVID-19 positive cases. Despite the clear necessity of these medical tests, many countries are still struggling to acquire them. This fact clearly indicates the necessity of a large-scale, cheap, fast, and accurate alternative pre-screening tool that can be used for the diagnosis of COVID-19 while waiting for the medical tests. To this end, a novel end-to-end transfer learning-based deep learning approach that uses only a given cough sound for the diagnosis of COVID-19 was proposed in this study.The proposed models employed various pre-trained deep neural networks, namely, VGG19, ResNet50V2, DenseNet121, and MobileNet, via the transfer-learning technique. Then, these models were evaluated on a gold standard dataset, namely, Cambridge Data. According to the experimental result, the proposed model, which employed the MobileNet via the transfer-learning technique, provided the best accuracy, 86.42 %, and outperformed the state-of-the-art. Thus, the proposed model has the potential to provide automated COVID-19 diagnosis in an easily applicable and fast yet accurate way.

The term of single board computer is used for computers which has built on a single circuit board with microprocessor, memory, input/output and other required units. In this study, control of a mobile robot in real time using Raspberry Pi which is a type of single board computer is performed via internet. In the developed system, all of the units in the mobile robot are controlled directly by Raspberry Pi, an additional microcontroller system is not used. Thus, without additional control equipment, a low cost and open source mobile robot design has been carried out.