A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned
The successful early diagnosis of brain tumors plays a major role in improving the treatment
outcomes and thus improving patient survival. Manually evaluating the numerous magnetic …
outcomes and thus improving patient survival. Manually evaluating the numerous magnetic …
A comprehensive survey on the detection, classification, and challenges of neurological disorders
Simple Summary This study represents a resourceful review article that can deliver
resources on neurological diseases and their implemented classification algorithms to …
resources on neurological diseases and their implemented classification algorithms to …
Deep-AntiFP: Prediction of antifungal peptides using distanct multi-informative features incorporating with deep neural networks
World widely, Fungal infections have become a serious issue for human beings. Fungal
infections normally happen once invading fungus appear on a specific area of the body and …
infections normally happen once invading fungus appear on a specific area of the body and …
Brain tumor classification using the fused features extracted from expanded tumor region
In this study, a brain tumor classification method using the fusion of deep and shallow
features is proposed to distinguish between meningioma, glioma, pituitary tumor types and …
features is proposed to distinguish between meningioma, glioma, pituitary tumor types and …
Segmentation and classification of breast cancer using novel deep learning architecture
S Ramesh, S Sasikala, S Gomathi, V Geetha… - Neural Computing and …, 2022 - Springer
Breast cancer is one of the most frequent cancers in women, and it has a higher mortality
rate than other cancers. As a result, early detection is critical. In computer-assisted disease …
rate than other cancers. As a result, early detection is critical. In computer-assisted disease …
Machine learning assisted methodology for multiclass classification of malignant brain tumors
Analysis of malignant and non-malignant brain tumors is done using a computer-aided
diagnosis system by practitioners worldwide. Radiologists refer computer-assisted …
diagnosis system by practitioners worldwide. Radiologists refer computer-assisted …
Computer-assisted brain tumor type discrimination using magnetic resonance imaging features
Medical imaging plays an integral role in the identification, segmentation, and classification
of brain tumors. The invention of MRI has opened new horizons for brain-related research …
of brain tumors. The invention of MRI has opened new horizons for brain-related research …
A novel extended Kalman filter with support vector machine based method for the automatic diagnosis and segmentation of brain tumors
B Chen, L Zhang, H Chen, K Liang, X Chen - Computer Methods and …, 2021 - Elsevier
Background Brain tumors are life-threatening, and their early detection is crucial for
improving survival rates. Conventionally, brain tumors are detected by radiologists based on …
improving survival rates. Conventionally, brain tumors are detected by radiologists based on …
Brain tumor detection using deep learning and image processing
AS Methil - … conference on artificial intelligence and smart …, 2021 - ieeexplore.ieee.org
Brain Tumor Detection is one of the most difficult tasks in medical image processing. The
detection task is difficult to perform because there is a lot of diversity in the images as brain …
detection task is difficult to perform because there is a lot of diversity in the images as brain …
SDCT-AuxNetθ: DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis
Acute lymphoblastic leukemia (ALL) is a pervasive pediatric white blood cell cancer across
the globe. With the popularity of convolutional neural networks (CNNs), computer-aided …
the globe. With the popularity of convolutional neural networks (CNNs), computer-aided …