Detection and Localization of EarlyStage Multiple Brain Tumors Using a Hybrid Technique of PatchBased Processing, kmeans Clustering and Object Counting Brain tumors are a major health problem that affect the lives of many people. These tumors are classified as benign or cancerous. The latter can be fatal if not properly diagnosed and treated. Therefore, the diagnosis of brain tumors at the early stages of their development can significantly improve the chances of patientxe2x80x99s full recovery after treatment. In addition to laboratory analyses, clinicians and surgeons extract information from medical images, recorded by various systems such as magnetic resonance imaging (MRI), Xray, and computed tomography (CT). The extracted information is used to identify the essential characteristics of brain tumors (location, size, and type) in order to achieve an accurate diagnosis to determine the most appropriate treatment protocol. In this paper, we present an automated machine vision technique for the detection and localization of brain tumors in MRI images at their very early stages using a combination of kmeans clustering, patchbased image processing, object counting, and tumor evaluation. The technique was tested on twenty real MRI images and was found to be capable of detecting multiple tumors in MRI images regardless of their intensity level variations, size, and location including those with very small sizes. In addition to its use for diagnosis, the technique can be integrated into automated treatment instruments and robotic surgery systems.

Dilated Capsule Network for Brain Tumor Type Classification Via MRI Segmented Tumor Region Brain tumor recently is considered among the deadliest cancers according to research statistics and have several categories, based on the different characteristics of the tumor. Early detection of the tumor types help to devise treatment plans and achieve high survival rate. Human inspection is noted to be cost effective, error prone and timeconsuming, which have led the interest in Convolutional Neural Networks (CNNs) to automatize the problem. However, CNNs fail to consider the precise location of the features as beneficial, which is harmful, because tumor location and its relationship with the surrounding tissue provide high influence on the brain tumor type. In addition, the CNNs require large amount of dataset for accurate training and prediction. CNNs increasingly reduce image resolution, which result to decrease in classification accuracy. In this work, we incorporated recently developed Capsule Networks (CapsNets) which overcome these drawbacks. The focused contribution is to enhance CapsNets with dilation to maintain the image resolution and improve classification accuracy. We proposed a less trainable CapsNet architecture for brain tumor classification, which takes the segmented tumor regions as inputs within the structure and has the capability of ensuring an increase focus of the CapsNets. While the baseline CapsNets consist of single convolutional layer, our proposed model introduced multiple convolutional layers which achieved an improved performance of 95.54% compared to the related works. Our results indicate that the proposed approach can improve brain tumor classification problem.