Brain diseases are mainly caused by abnormal growth of brain cells that may damage the brain structure, and eventually will lead to malignant brain cancer. An early diagnosis to enable decisive treatment using a Computer-Aided Diagnosis (CAD) system has major challenges, especially accurate detection of different diseases in the magnetic resonance imaging (MRI) images. In this paper, a new Deep Convolutional Neural Network (DCNN) architecture is proposed for effective diagnosis of glioma, meningioma and pituitary. The architecture uses batch normalization for fast training with a higher learning rate and ease initialization of the layer weights. The proposed architecture is a computationally lightweight model with a small number of convolutional, max-pooling layers and training iterations. A demonstrative comparison between the proposed architecture and other discussed models in this paper is conducted. An outstanding competitive accuracy is achieved of 97.72% overall, 99% in detecting glioma, 98.26% in detecting meningioma, 95.95% in detecting pituitary and 97.14% in detecting normal images when tested on a dataset with 3394 MRI images. Experimental results prove the robustness of the proposed architecture which has increased the detection accuracy of a variety of brain diseases in a short time.INDEX TERMS brain tumors, deep convolutional neural network, image processing, MRI images I. INTRODUCTION

Recently, the COVID-19 pandemic is considered the most severe infectious disease because of its rapid spreading. Radiologists still lack sufficient knowledge and experience for accurate and fast detecting COVID-19. What exacerbates things is the significant overlap between Pneumonia symptoms and COVID-19, which confuses the radiologists. It’s widely agreed that the early detection of the infected patient increases his likelihood of recovery. Chest X-ray images are considered the cheapest radiology images, and their devices are available widely. This study introduces an effective Deep Convolutional Neural Network (DCNN) called “DeepChest” for fast and accurate detection for both COVID-19 and Pneumonia in chest X-ray images. “DeepChest” runs with a small number of convolutional layers, a small number of max-pooling layers, and a small number of training iterations compared with the recent approaches and the state-of-the-art of DCNN. We conducted the experimental evaluations of the proposed approach on a data set with 7512 chest X-ray images. The proposed approach achieves an accuracy of 96.56% overall, 99.40% in detecting COVID-19, and 99.32% in detecting Pneumonia. In actual practice, the presented approach can be used as a computer-aided diagnosis tool to get accurate results in detecting Pneumonia and COVID-19 in chest X-ray images.

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