Background: A World Health Organization (WHO) February 2018 report recently has shown that the rate of deaths because of brain or central nervous system (CNS) cancer has the highest rate in the Asian continent. Timely and accurate diagnosis of brain tumor is crucial where small errors pose many risks to treatment. Classifying the types of tumors is an important factor in targeted treatment. Since tumor diagnosis is highly invasive, time-consuming, and costly, there is an urgent need for a precise tool to develop a non-invasive, cost-effective, and efficient tool for brain tumor description and grade estimation. Brain scans by using magnetic resonance imaging (MRI), computed tomography (CT), and other imaging techniques are fast and safe to detect tumors. Methods: In this paper, we used a standard dataset containing 3064 images from different skull views. The size and position of tumors at different angles make it difficult to detect the tumor in the specimens. This MRI dataset consisted of 3064 slices and 1047 coronal images. Coronal images were recorded from behind. Axial images taken from above included 990 images. Also, there were 1027 sagittal images extracted from the skull side. Images in this dataset belonged to 233 patients. The dataset consisted of 708 Meningioma, 1426 Glioma, and 930 Pituitary tumors; thus, we isolated images from different angles of sagittal, coronal, and axial images and then trained them in different categories by using Inception-V3 and Resent, which are deep learning classification methods to make this process more accurate and faster. Results: Finally, by adjusting the hyper-parameters of each of these methods with performing pre-processing and weighting combinations, we obtained an acceptable evaluation compared to previous methods.

Background: Scoliosis is a three-dimensional deformity of the spine that is commonly assessed through measuring the Cobb angle. Objectives: In this study, a Cobb angle measurement decision support system (CaMDSS) was presented to provide a repeatable and reproducible procedure for Cobb angle measurement in idiopathic scoliosis patients. Methods: We used the OpenCV and the Numpy library for image processing and system design. A series of 98 anterior-posterior radiographs from patients diagnosed with idiopathic scoliosis were used to assess the repeatability and reproducibility of CaMDSS. Five independent observers performed the measurements, and each image was analyzed by each observer three times with a minimum interval of two weeks between measurements. Both the intra-and inter-observer reliability were obtained using the single measure intraclass correlation coefficient (ICC) value. The mean absolute difference (MAD) and the standard error measurement (SEM) were calculated for all corresponding intra-and inter-observer reliability estimates. Results: Statistical results for the inter-observer analysis showed that the MAD between manual and CaMDSS was less than 3º, and the ICCs ranged from 0.94 to 0.99. The combined SEM between all five observers for intra-observer measurements of the manual method and CaMDSS was 1.79º and 1.27º, respectively. The inter-observer reliability of CaMDSS was excellent as the ICC value of 0.97 with 95% CI was obtained. The CaMDSS mean absolute difference was 2.18 ± 2.01 degrees. Conclusion: Our study showed CaMDSS was an efficient and reliable method to assess the scoliotic curvature in Thoraco-Lumbar standing radiographs with the possibility of expediting clinic visits, ensuring the reliability of calculation, and decreasing the patient's exposure to radiation.