Image Segmentation is a method of dividing a group of data into different segments which has uniform characteristics. It is a computational process done by automatically and broadly used in medical imaging for the diagnosis and treatment of various diseases in human body. Brain is the supreme part of central nervous system which controls and coordinating all the activities of the body. Analyzing the complex structure of brain and finding the abnormalities is very challenging and time consuming task for medical practitioners. So brain image segmentation is very important but most challenging problem among medical images. This paper depicts the different challenges and applications of brain image segmentation which can be extended in many directions of brain images. In this paper we review the different modalities of medical images like MRI, CT, and SPECT etc. We highlight the various applications of these modalities which are used in image registration, detection and retrieval for the purpose of proper diagnosis of brain disorders.

Nowadays the most exciting technology breakthrough has been the rise of the deep learning. In computer vision Convolutional Neural Networks (CNN or ConvNet) are the default deep learning model used for image classification problems. In these deep network models, feature extraction is figure out by itself and these models tend to perform well with huge amount of samples. Herein we explore the impact of various Hyper-Parameter Optimization (HPO) methods and regularization techniques with deep neural networks on FashionMNIST (F-MNIST) dataset which is proposed by Zalando Research. We have proposed deep ConvNet architectures with Data Augmentation and explore the impact of this by configuring the hyperparameters and regularization methods. As deep learning requires a lots of data, the insufficiency of image samples can be expand through various data augmentation methods like Cropping, Rotation, Flipping, and Shifting. The experimental results show impressive results on this new benchmarking dataset F-MNIST

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