Nowadays, quality improvement and increased accessibility to patient data, at a reasonable cost, are highly challenging tasks in healthcare sector. Internet of Things (IoT) and Cloud Computing (CC) architectures are utilized in the development of smart healthcare systems. These entities can support real-time applications by exploiting massive volumes of data, produced by wearable sensor devices. The advent of evolutionary computation algorithms and Deep Learning (DL) models has gained significant attention in healthcare diagnosis, especially in decision making process. Skin cancer is the deadliest disease which affects people across the globe. Automatic skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin lesions. The current research article presents a new skin lesion diagnosis model i.e., Deep Learning with Evolutionary Algorithm based Image Segmentation (DL-EAIS) for IoT and cloud-based smart healthcare environments. Primarily, the dermoscopic images are captured using IoT devices, which are then transmitted to cloud servers for further diagnosis. Besides, Backtracking Search optimization Algorithm (BSA) with Entropy-Based Thresholding (EBT) i.e., BSA-EBT technique is applied in image segmentation. Followed by, Shallow Convolutional Neural Network (SCNN) model is utilized as a feature extractor. In addition, Deep-Kernel Extreme Learning Machine (D-KELM) model is employed as a classification model to determine the class labels of dermoscopic images. An extensive set of simulations was conducted to validate the performance of the presented method using benchmark dataset. The experimental outcome infers that

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Intracranial epidermoids are benign tumours arising from retained ectodermal implants. Spontaneous rupture is an important though rare complication of intracranial epidermoid. To our knowledge, MR findings in spontaneous epidermoid rupture have not been well described to date. We report the case of a 60-year-old man who presented with a two day history of headache, altered sensorium and left hemiplegia. A diagnosis of ruptured epidermoid was made based on MR imaging findings which were subsequently proven by histopathology.

Aim: To evaluate the solid focal liver lesions by Shear Wave Sonoelastography (SWE) and correlate Shear Wave Sonoelastography findings with that of FNAC. Methods: 50 patients who were diagnosed to have solid focal liver lesions on sonography during the period August 2017 to September 2019 at JSS Medical College and Hospital, Mysuru underwent Shear Wave Sonoelastography [SWE], following which patient underwent ultrasound guided FNAC for histological evaluation. Results: Benign vs. malignant hepatic lesions could be differentiated using a cut off value of 25 kPa. The overall sensitivity & specificity of SWE was found to be 66% and 30% respectively as a standalone technique, however the predicative accuracy of SWE in conjunction with gray scale sonographic findings was 91.4%. Conclusion: Shear wave elastography can be used as an adjunct in routine sonological practice to evaluate solid focal lesions of the liver. It can help to categorize benign versus malignant lesions.