Mudassir Khalil¹, Muhammad Imran Sharif^2,*, Ahmed Naeem³, Muhammad Umar Chaudhry¹, Hafiz Tayyab Rauf^4,*, Adham E. Ragab⁵

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2031-2047, 2023, DOI:10.32604/cmc.2023.043687

Abstract Early detection of brain tumors is critical for effective treatment planning. Identifying tumors in their nascent stages can significantly enhance the chances of patient survival. While there are various types of brain tumors, each with unique characteristics and treatment protocols, tumors are often minuscule during their initial stages, making manual diagnosis challenging, time-consuming, and potentially ambiguous. Current techniques predominantly used in hospitals involve manual detection via MRI scans, which can be costly, error-prone, and time-intensive. An automated system for detecting brain tumors could be pivotal in identifying the disease in its earliest phases. This research applies several data augmentation techniques… More >

Mohammed Aly^1,*, Abdullah Shawan Alotaibi²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 557-582, 2023, DOI:10.32604/cmc.2023.042493

Abstract Tumor segmentation is a valuable tool for gaining insights into tumors and improving treatment outcomes. Manual segmentation is crucial but time-consuming. Deep learning methods have emerged as key players in automating brain tumor segmentation. In this paper, we propose an efficient modified U-Net architecture, called EMU-Net, which is applied to the BraTS 2020 dataset. Our approach is organized into two distinct phases: classification and segmentation. In this study, our proposed approach encompasses the utilization of the gray-level co-occurrence matrix (GLCM) as the feature extraction algorithm, convolutional neural networks (CNNs) as the classification algorithm, and the chi-square method for feature selection.… More >

Tawfeeq Shawly¹, Ahmed Alsheikhy^2,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 425-443, 2023, DOI:10.32604/cmc.2023.040561

Abstract According to the World Health Organization (WHO), Brain Tumors (BrT) have a high rate of mortality across the world. The mortality rate, however, decreases with early diagnosis. Brain images, Computed Tomography (CT) scans, Magnetic Resonance Imaging scans (MRIs), segmentation, analysis, and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages. For physicians, diagnosis can be challenging and time-consuming, especially for those with little expertise. As technology advances, Artificial Intelligence (AI) has been used in various domains as a diagnostic tool and offers promising outcomes. Deep-learning techniques are especially useful and have achieved… More >

Xiaoliang Lei¹, Xiaosheng Yu^2,*, Hao Wu³, Chengdong Wu^2,*, Jingsi Zhang²

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3797-3811, 2023, DOI:10.32604/cmc.2023.042069

Abstract Accurate tumor segmentation from brain tissues in Magnetic Resonance Imaging (MRI) imaging is crucial in the pre-surgical planning of brain tumor malignancy. MRI images’ heterogeneous intensity and fuzzy boundaries make brain tumor segmentation challenging. Furthermore, recent studies have yet to fully employ MRI sequences’ considerable and supplementary information, which offers critical a priori knowledge. This paper proposes a clinical knowledge-based hybrid Swin Transformer multimodal brain tumor segmentation algorithm based on how experts identify malignancies from MRI images. During the encoder phase, a dual backbone network with a Swin Transformer backbone to capture long dependencies from 3D MR images and a… More >

Muhammad Attique Khan^1,2, Reham R. Mostafa³, Yu-Dong Zhang², Jamel Baili⁴, Majed Alhaisoni⁵, Usman Tariq⁶, Junaid Ali Khan¹, Ye Jin Kim⁷, Jaehyuk Cha^7,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3029-3047, 2023, DOI:10.32604/cmc.2023.038838

Abstract Manual diagnosis of brain tumors using magnetic resonance images (MRI) is a hectic process and time-consuming. Also, it always requires an expert person for the diagnosis. Therefore, many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the literature. This paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization algorithm. NasNet-Mobile, a pre-trained deep learning model, has been fine-tuned and two-way trained on original and enhanced MRI images. The haze-convolutional neural network (haze-CNN) approach is developed and employed on the original images for contrast enhancement.… More >

Kainat Nazir¹, Tahir Mustafa Madni¹, Uzair Iqbal Janjua¹, Umer Javed², Muhammad Attique Khan³, Usman Tariq⁴, Jae-Hyuk Cha^5,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2861-2877, 2023, DOI:10.32604/cmc.2023.039181

Abstract Brain tumor significantly impacts the quality of life and changes everything for a patient and their loved ones. Diagnosing a brain tumor usually begins with magnetic resonance imaging (MRI). The manual brain tumor diagnosis from the MRO images always requires an expert radiologist. However, this process is time-consuming and costly. Therefore, a computerized technique is required for brain tumor detection in MRI images. Using the MRI, a novel mechanism of the three-dimensional (3D) Kronecker convolution feature pyramid (KCFP) is used to segment brain tumors, resolving the pixel loss and weak processing of multi-scale lesions. A single dilation rate was replaced… More >

Wajiha Rahim Khan¹, Tahir Mustafa Madni¹, Uzair Iqbal Janjua¹, Umer Javed², Muhammad Attique Khan³, Majed Alhaisoni⁴, Usman Tariq⁵, Jae-Hyuk Cha^6,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 647-664, 2023, DOI:10.32604/cmc.2023.039188

Abstract Segmenting brain tumors in Magnetic Resonance Imaging (MRI) volumes is challenging due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled datasets. However, 3D networks can be computationally expensive and require significant training resources. This research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or accuracy. The proposed model, called Hybrid Attention-Based Residual Unet (HA-RUnet), is based on the Unet architecture and utilizes residual blocks to extract low-… More >

Muhammad Irfan¹, Ahmad Shaf^2,*, Tariq Ali², Umar Farooq², Saifur Rahman¹, Salim Nasar Faraj Mursal¹, Mohammed Jalalah¹, Samar M. Alqhtani³, Omar AlShorman⁴

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 711-729, 2023, DOI:10.32604/cmc.2023.038176

Abstract A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated… More >

R. D. Dhaniya¹, K. M. Umamaheswari^2,*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1129-1143, 2023, DOI:10.32604/iasc.2023.035905

Abstract Current revelations in medical imaging have seen a slew of computer-aided diagnostic (CAD) tools for radiologists developed. Brain tumor classification is essential for radiologists to fully support and better interpret magnetic resonance imaging (MRI). In this work, we reported on new observations based on binary brain tumor categorization using HYBRID CNN-LSTM. Initially, the collected image is pre-processed and augmented using the following steps such as rotation, cropping, zooming, CLAHE (Contrast Limited Adaptive Histogram Equalization), and Random Rotation with panoramic stitching (RRPS). Then, a method called particle swarm optimization (PSO) is used to segment tumor regions in an MR image. After… More >

Amarendra Reddy Panyala^1,2, M. Baskar^3,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3883-3899, 2023, DOI:10.32604/csse.2023.037050

Abstract The deep learning models are identified as having a significant impact on various problems. The same can be adapted to the problem of brain tumor classification. However, several deep learning models are presented earlier, but they need better classification accuracy. An efficient Multi-Feature Approximation Based Convolution Neural Network (CNN) model (MFA-CNN) is proposed to handle this issue. The method reads the input 3D Magnetic Resonance Imaging (MRI) images and applies Gabor filters at multiple levels. The noise-removed image has been equalized for its quality by using histogram equalization. Further, the features like white mass, grey mass, texture, and shape are… More >