Online ISSN: 2515-8260

Keywords : Brain tumor

Segmentation on Brain Cancer Disease using Deep Learning Techniques

J. Josphin Mary; R. Charanya; V. Shanthi; G. Sridevi; Meda Srinivasa Rao

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1439-1446

Segmenting brain tumors is a major challenge in the production of scientific pictures. To order to maximize care outcomes and increasing the hospital success rate, early detection of brain tumors plays an important part. A challenging and time-consuming job is the manual segmentation of brain tumors from large quantities of MRI images produced in clinical routine. Automatic brain tumor segmentation is possible. This article aims to analyze strategies for the segmentation of brain tumors dependent on MRI. Automatic segmentation using deep learning approaches has recently been proven common because these approaches accomplish the latest findings much better than other methods would solve this issue. Deep learning approaches may also provide for effective analysis and unbiased interpretation of vast volumes of picture evidence dependent on MRI. There are many papers on MRI based brain tumor segmentation which focus on traditional methods. Different from others, we concentrate on the recent trend in the field of deep learning. Next, the brain tumors and techniques for segmenting the brain tumor are added. Then, the new architectures are explored with a emphasis on the current development in deep learning methods. Finally, an evaluation is introduced and further improvements are discussed to standardize brain tumor segmentation procedures dependent on MRI in the day-to-day clinical practice.

Detection of tumor in brain depended on k-means clustering using GUI

Vishal Goyal; Vinay Kumar Deolia

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 560-569

The main purpose of this project is to identify the brain tumor utilizing graphical user interface in MATLAB software. Neoplasm is a nun manageable growth of clump in brain tissues. The identification of tumefaction can be done by using either CT (computerized tomography) scan or MRI (magnetic resonance imaging). Mostly the MRI images are preferred over CT scan as they describe functional information of the tumefaction. The approach to design this paper involves four stages: - Pre-processing, edge detection, fuzzy-means clustering, followed by segmentation. This integrated approach allows the segmentation of swelling tissues with accuracy and reproducibility compared to manual segmentation. Finally, the tumefaction affected region is clearly displayed using segmentation.

Brain Tumor And Intracranial Haemorrhage Feature Extraction And Classification Using Conventional And Deep Learning Methods

R. Aruna Kirithika; S. Sathiya; M. Balasubramanian; P. Sivaraj

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 7, Pages 237-258

Presently, brain tumor (BT) and Intracranial hemorrhage (ICH) detection and classification processes become essential to save human lives. Automated diagnosis model using deep learning (DL) models finds useful to attain improved diagnostic outcome. This paper presents an ensemble of handcrafted and deep features for BT and ICH diagnosis. The proposed model comprises of three important processes, such as preprocessing, feature extraction and classification. The preprocessing of the input image takes place in three ways namely skull stripping, bilateral filtering (BF) and contrast limited adaptive histogram equalization (CLAHE) based contrast enhancement. In addition, scale invariant feature transform (SIFT) and AlexNet models are used for feature extraction process. In order to classify the existence of BT and ICH, two classification models is carried out such as gaussian naïve bayes (GNB) and random forest (RF).For validating the effective diagnostic performance of the proposed model, a set of simulations were carried out to determine the different class labels. The simulation outcome indicated the effective performance with the maximum sensitivity of 92.41%, specificity of 100%, and accuracy of 94.26%.