A Comparative Study on Brain Tumor Diagnosis Techniques Using MRI Image Processing

Abstract

Image processing is a technique to carry out certain operations on an image so as to obtain some helpful information from it i.e., an enhanced image would be more advantageous. It is a similar to that of signal processing where input would be the image and the output obtained through this processing would be attributes/characteristics corresponding to that image. Currently, Image processing is one among the trending technologies. It is definitely one significant study area in the fields of engineering and computer science. Medical images have a vital role in the diagnosis of disorders & for checking the accurate functioning of organs. Hence, this is an active research area where several methods are being used and developed in order to make diagnosis facile. This medicinal imaging technology gave the doctors an insight for diagnosis of internal parts of the body. Also it helped doctors in performing Minimally Invasive Surgery, more commonly called as the keyhole surgeries which is done by inserting long and thin instruments into the body through small incisions to reach the internal organs instead of traditional operation techniques. The brain is the utmost important internal organ in our body and tumors effecting it could be very critical and hazardous situation. The brain tumor is a soft intracranial mass made up by irregular growth of cells of the tissue in the brain or around the brain. MRI Imaging play a vital role in brain tumors for evaluation, diagnosis and treatment arrangement.

Performance Enhancement in Facial Emotion Classification Through Noise-Injected FERCNN Model: A Comparative Analysis

“…Deep Neural Networks (DNN) [23], particularly Convolutional Neural Networks (CNN), have gained popularity for their efficacy in learning and recognizing features. Before the advent of deep learning, various approaches were employed for image classification into appropriate classes, including dance action identification [24], brain tumor identification [25][26][27], plant disease identification [28,29], among other applications [30,31]. Although CNNs prove computationally expensive and architecturally complex compared to other facial emotion recognition systems, these issues have been mitigated by recent technological advances and resource availability.…”

Facial emotion recognition using deep convolutional neural network and smoothing, mixture filters applied during preprocessing stage

“…Convolutional neural networks are not only for facial emotion recognition that we applied in the under described research, but also in several classifications such as human disease classification [11], [12], and plant disease classification [13]. Before deep CNN quite popular, the image classification uses a different machine learning algorithms and methods to classify in applications like brain tumor [14], [15], Plant disease [16], [17] and other [18], [19].…”