Maham Khan¹, Syed Adnan Shah¹, Tenvir Ali², Quratulain², Aymen Khan², Gyu Sang Choi^3,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5005-5020, 2022, DOI:10.32604/cmc.2022.023007

Abstract Brain tumors are considered as most fatal cancers. To reduce the risk of death, early identification of the disease is required. One of the best available methods to evaluate brain tumors is Magnetic resonance Images (MRI). Brain tumor detection and segmentation are tough as brain tumors may vary in size, shape, and location. That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’. So an automated brain tumor detection and segmentation is required. This work suggests a Region-based Convolution Neural Network (RCNN) approach for automated brain tumor identification and segmentation using MR images,… More >

T. Jeslin^1,*, J. Arul Linsely²

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 171-182, 2022, DOI:10.32604/cmc.2022.020255

Abstract Brain cancer is the premier reason for cancer deaths all over the world. The diagnosis of brain cancer at an initial stage is mediocre, as the radiologist is ineffectual. Different experiments have been conducted and demonstrated clearly that the algorithms for nodule segmentation are unsuccessful. Therefore, the research has consolidated incremental clustering focused on superpixel segmentation as an appropriate optimization approach for the accurate segmentation of pulmonary nodules. The key aim of the research is to refine brain CT images to accurately distinguish tumors and the segmentation of small-scale anomalous nodules in the brain region. In the beginning stage, an… More >

R. Rajaragavi^1,*, S. Palanivel Rajan²

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 1-14, 2022, DOI:10.32604/iasc.2022.021206

Abstract A brain tumor is a portion of uneven cells, need to be detected earlier for treatment. Magnetic Resonance Imaging (MRI) is a routinely utilized procedure to take brain tumor images. Manual segmentation of tumor is a crucial task and laborious. There is a need for an automated system for segmentation and classification for tumor surgery and medical treatments. This work suggests an efficient brain tumor segmentation and classification based on deep learning techniques. Initially, Squirrel search optimized bidirectional ConvLSTM U-net with attention gate proposed for brain tumour segmentation. Then, the Hybrid Deep ResNet and Inception Model used for classification. Squirrel… More >

Juhong Tie^1,2,*, Hui Peng², Jiliu Zhou^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 427-445, 2021, DOI:10.32604/cmes.2021.014107

Abstract The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automatically segment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancing tumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, it is very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantages of DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks. We used dense blocks in the encoder part and residual blocks in the decoder part. The number… More >

Linguo Li^1,2, Shujing Li^1,*, Jian Su³

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2355-2366, 2021, DOI:10.32604/cmc.2021.019409

Abstract Brain tumor is one of the most common tumors with high mortality. Early detection is of great significance for the treatment and rehabilitation of patients. The single channel convolution layer and pool layer of traditional convolutional neural network (CNN) structure can only accept limited local context information. And most of the current methods only focus on the classification of benign and malignant brain tumors, multi classification of brain tumors is not common. In response to these shortcomings, considering that convolution kernels of different sizes can extract more comprehensive features, we put forward the multi-size convolutional kernel module. And considering that… More >

Ahsan Aziz¹, Muhammad Attique¹, Usman Tariq², Yunyoung Nam^3,*, Muhammad Nazir¹, Chang-Won Jeong⁴, Reham R. Mostafa⁵, Rasha H. Sakr⁶

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2653-2670, 2021, DOI:10.32604/cmc.2021.018606

Abstract Owing to technological developments, Medical image analysis has received considerable attention in the rapid detection and classification of diseases. The brain is an essential organ in humans. Brain tumors cause loss of memory, vision, and name. In 2020, approximately 18,020 deaths occurred due to brain tumors. These cases can be minimized if a brain tumor is diagnosed at a very early stage. Computer vision researchers have introduced several techniques for brain tumor detection and classification. However, owing to many factors, this is still a challenging task. These challenges relate to the tumor size, the shape of a tumor, location of… More >

J. Jayanthi^1,*, M. Kavitha², T. Jayasankar³, A. Sagai Francis Britto⁴, N. B. Prakash⁵, Mohamed Yacin Sikkandar⁶, C. Bharathiraja⁷

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4191-4203, 2021, DOI:10.32604/cmc.2021.012252

Abstract Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era. Among various cancers identified so far, glioma, a type of brain tumor, is one of the deadliest cancers, and it remains challenging to the medicinal world. The only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the disease. Early diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of time. From the captured image, the… More >

Tapan Kumar Das¹, Pradeep Kumar Roy², Mohy Uddin³, Kathiravan Srinivasan¹, Chuan-Yu Chang^4,*, Shabbir Syed-Abdul⁵

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2413-2429, 2021, DOI:10.32604/cmc.2021.016698

Abstract Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s inspection. However, the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory, clinical mapping, and delusion tracing. In this proposed study, a deep learning based framework that employs deep convolution neural network (Deep-CNN), by utilizing both clinical presentations and conventional magnetic resonance imaging (MRI) investigations, for diagnosing tumors is explored. This research aims to develop a model that can be used for abnormality detection over MRI data… More >

Ayesha Bin T. Tahir¹, Muhamamd Attique Khan¹, Majed Alhaisoni², Junaid Ali Khan¹, Yunyoung Nam^3,*, Shui-Hua Wang⁴, Kashif Javed⁵

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1099-1116, 2021, DOI:10.32604/cmc.2021.015154

Abstract Background: A brain tumor reflects abnormal cell growth. Challenges: Surgery, radiation therapy, and chemotherapy are used to treat brain tumors, but these procedures are painful and costly. Magnetic resonance imaging (MRI) is a non-invasive modality for diagnosing tumors, but scans must be interpretated by an expert radiologist. Methodology: We used deep learning and improved particle swarm optimization (IPSO) to automate brain tumor classification. MRI scan contrast is enhanced by ant colony optimization (ACO); the scans are then used to further train a pretrained deep learning model, via transfer learning (TL), and to extract features from two dense layers. We fused… More >

Oday Ali Hassen¹, Sarmad Omar Abter², Ansam A. Abdulhussein³, Saad M. Darwish^4,*, Yasmine M. Ibrahim⁴, Walaa Sheta⁵

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 961-981, 2021, DOI:10.32604/cmc.2021.014404

Abstract Medical image segmentation has consistently been a significant topic of research and a prominent goal, particularly in computer vision. Brain tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy preparation. To prevent or minimize manual segmentation error, automated tumor segmentation, and detection became the most demanding process for radiologists and physicians as the tumor often has complex structures. Many methods for detection and segmentation presently exist, but all lack high accuracy. This paper’s key contribution focuses on evaluating machine learning… More >