Unsupervised CNN model for Sclerosis Detection

Sclerosis detection using brain magnetic resonant imaging (MRI) im-ages is challenging task. With the promising results for variety of ap-plications in terms of classification accuracy using of deep neural net-work models, one can use such models for sclerosis detection. The fea-tures associated with sclerosis is important factor which is highlighted with contrast lesion in brain MRI images. The sclerosis classification initially can be considered as binary task in which the sclerosis seg-mentation can be avoided for reduced complexity of the model. The sclerosis lesion show considerable impact on the features extracted us-ing convolution process in convolution neural network models. The images are used to train the convolutional neural network composed of 35 layers for the classification of sclerosis and normal images of brain MRI. The 35 layers are composed of combination of convolutional lay-ers, Maxpooling layers and Upscaling layers. The results are com-pared with VGG16 model and results are found satisfactory and about 92% accuracy is seen for validation set.

RIGA FEDE DISEASE- AN UNUSUAL PRESENTATION

A 7-year-old female child came to hospital with history of swelling over left side of tongue from 10 days. Swelling was identied accidentally by her mother. There were no complaints per se from the patient. On examination there was 2x1cm smooth swelling over dorsal surface of the left side of tongue in anterior one-third. It was a rm swelling with well-dened margins. Magnetic Resonant Imaging of neck with contrast was done which showed a T1 Hypointense with T2 hyperintense peripherally enhancing lesion of the left genioglossus which was suggestive of Fibroma/Dermoid cyst/mucous retention cyst. Cyst was excised in toto under General Anaesthesia and sent for Histopathological examination which showed features of Riga Fede disease (Eosinophilic ulcer of the tongue). Patient was followed up for 3 months and no signs of recurrence were seen.

Congenital Bladder and Urethral Agenesis: Two Case Reports and Management

Background. Agenesis of the bladder and urethra is a rare congenital anomaly, with a very few living cases reported in the literature so far. Case Presentation. We are reporting two female patients (3 and 6 years old) with bladder and urethral agenesis who presented with urinary incontinence. In both patients, magnetic resonant imaging (MRI) revealed a case of bladder and urethral agenesis with normal ureters draining into the vagina. Patients underwent a neobladder and conduit creation surgery. The neobladder was constructed from the whole cecum and a part of the ascending colon, followed by an anastomose of the ureters into the neobladder in a nonrefluxing fashion; the appendix was used simultaneously as a continent catheterizable conduit. The two patients attained urinary continence postoperatively. Conclusion. We reported two cases of bladder agenesis, and for the first time, we have performed neobladder creation surgery using the cecum and ascending colon. One-year follow-up did not reveal any complications.

Bilateral Anophthalmia: Literature, Review and Case Report

Introduction: Anophthalmia which is the congenital absence of the eyes could be bilateral or congenital, it occurs globally although it is rare. It is usually in association with other systemic congenital abnormalities. Case Report: A case of a female baby delivered at a primary health center in Port Harcourt, Nigeria who had bilateral anophthalmia is reported here along with review of literature Magnetic Resonant Imaging (MRI) of brain and orbit was used to confirm the diagnosis which showed absence of both globes, with hypoplasia of the orbits as well as the extraocular muscles, the optic nerves could not be differentiated from the visualized intra-orbital structures. All other systems were essentially normal. This is an exceptional case in Port Harcourt, Nigeria, as literature search shows that no other case has been reported in Port Harcourt. She is currently being followed up at the paediatric, ophthalmology and ENT clinics of the University of Port Harcourt teaching hospital.

Wireless, battery-free subdermally implantable photometry systems for chronic recording of neural dynamics

Recording cell-specific neuronal activity while monitoring behaviors of freely moving subjects can provide some of the most significant insights into brain function. Current means for monitoring calcium dynamics in genetically targeted populations of neurons rely on delivery of light and recording of fluorescent signals through optical fibers that can reduce subject mobility, induce motion artifacts, and limit experimental paradigms to isolated subjects in open, two-dimensional (2D) spaces. Wireless alternatives eliminate constraints associated with optical fibers, but their use of head stages with batteries adds bulk and weight that can affect behaviors, with limited operational lifetimes. The systems introduced here avoid drawbacks of both types of technologies, by combining highly miniaturized electronics and energy harvesters with injectable photometric modules in a class of fully wireless, battery-free photometer that is fully implantable subdermally to allow for the interrogation of neural dynamics in freely behaving subjects, without limitations set by fiber optic tethers or operational lifetimes constrained by traditional power supplies. The unique capabilities of these systems, their compatibility with magnetic resonant imaging and computed tomography and the ability to manufacture them with techniques in widespread use for consumer electronics, suggest a potential for broad adoption in neuroscience research.

An Efficient Hemorrhage Detection System using Decision Tree Classifier

the visual representations of the inner constituents of body along with the functions of either organs or tissues comprising its physiology are developed in medical imaging. These images can be obtained by various techniques such as computed tomography (CT), magnetic resonant imaging (MRI), and x-ray. The objective of the system mentioned in this paper is to detect the presence of hemorrhage and to classify the type of it when detected. CT images are considered here to find the hemorrhage. Pre-processing techniques such as grayscale conversion, image resizing, edge detection and sharpening are done to make the input image suitable for further processing. After preprocessing the images go through morphological operations to help identify the shape related features in correspondence to the hemorrhage. Sobel and markers are used in the processed ct image to highlight the interested region. Then watershed algorithm is employed for the purpose of segmentation. The presence of hemorrhage can be detected as a result of segmentation. Once hemorrhage is detected feature extraction is done to classify its type. Active contours are drawn and features extracted are fed to the decision tree. The classifier helps in finding the type of hemorrhage with the detected features. The classifier result can be viewed, interpreted and evaluated by medical assistance. The aim of this research is to increase the chance of predicting hemorrhage in the image and then to classify its type. The proposed system classifies three types of hemorrhages. The average accuracy of the system in classifying the three types of hemorrhage is found as 98%