Multiple Myeloma Involving Left Hemicranium

Multiple myeloma (MM) is a malignant neoplasm of bone marrow affecting plasma cells. It is commonly seen as multiple punched-out lesions in the skull bone as a characteristic feature. Its presentation as hemicranial involvement with intracranial extension is rare. A 46-year-old male presented with left side scalp swelling, prominent over parietal region. X-ray showed multiple punched out lesions involving left hemicranium. CT and MRI brain showed intracranial extension of lesion without brain parenchyma invasion. He was treated with biopsy of lesion followed by chemotherapy.

The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network

The technology <span>of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% </span>accuracy.

Magnetic Resonance Imaging Brain and Electroencephalogram (EEG) in the Evaluation of New-Onset Seizures in a Tertiary Care Centre of Nepal

Introduction: Seizure is a common neurological condition with multiple etiological factors. This study aims to evaluate the role of magnetic resonance imaging (MRI) Brain and electroencephalography (EEG) in the diagnosis of new-onset seizures in the Nepalese population. Methods: A total of 106 patients aged between 7 to 85 years of age with first onset seizure, who underwent MRI and EEG were enrolled in the study. The sensitivity of MRI and EEG for the diagnosis of seizure when used in combination was compared with that of MRI or EEG alone. Results: Out of 106 patients, 58.5% (n=62) were males and 41.5% (n=44) were females. In 52.8% (n= 56) of the patients, there was epileptogenic lesion in MRI, and 39.6% (n=42) of the patients had an abnormal EEG. The combination of MRI with EEG was significantly better than either MRI or EEG alone in the diagnosis of seizures (p <0.001). Conclusion: MRI and EEG are frequently used for the evaluation of seizures. MRI Brain when used in combination with EEG significantly improves the diagnostic accuracy of seizures.