TY - JOUR
T1 - Brain tumor detection and screening using artificial intelligence techniques
T2 - Current trends and future perspectives
AU - Raghavendra, U.
AU - Gudigar, Anjan
AU - Paul, Aritra
AU - Goutham, T. S.
AU - Inamdar, Mahesh Anil
AU - Hegde, Ajay
AU - Devi, Aruna
AU - Ooi, Chui Ping
AU - Deo, Ravinesh C.
AU - Barua, Prabal Datta
AU - Molinari, Filippo
AU - Ciaccio, Edward J.
AU - Acharya, U. Rajendra
N1 - Funding Information:
Zacharaki et al. developed an algorithm using a combination of conventional and perfusion MRI coupled with a support vector machine (SVM) for differential tumor diagnosis. The developed classification scheme achieved high accuracy for most of the classification problems. The method used a statistical feature and Gabor texture for feature extraction [42]. Weili et al. developed an algorithm with unsupervised machine learning employing the pulse-coupled neural network (PCNN) technique for medical image segmentation. The suggested method improved the chargeable threshold function in PCNN and combined 2D Tsallis entropy to segment the image automatically. The resulting segmented images had stronger adaptability and better precision [43].Using a public dataset of 3064 T1-CE MR images and a data set of 3064 T1-CE MR pictures from 233 individuals who had one of three distinct forms of brain tumors, including gliomas (1426 images), meningiomas (708 images), and pituitary tumors, Allah et al. [77] presented a DL-based technique (930 images). The hardware utilized for this study included a graphics processing unit (GPU) P100, 2 TB of storage, and 12 GB of Memory. In their publication, Zahoor et al. [83] introduced a deep hybrid boosted & ensemble learning-based analysis of brain tumors utilizing MRI scans. To carry out their experiment, the scientists gathered 5058 photos, including 3064 photographs of tumors and 1994 images of healthy people. The simulations were run on a Core-I, i7-7500 CPU at 2.90 GHz with a Nvidia® GTX-1060 T that supports CUDA. The suggested model's computing cost was 5–7 h, with training taking 20–30 min for each epoch.
Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/9
Y1 - 2023/9
N2 - A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range of health issues. As malignant brain tumors grow rapidly, the mortality rate of individuals with this cancer can increase substantially with each passing week. Hence it is vital to detect these tumors early so that preventive measures can be taken at the initial stages. Computer-aided diagnostic (CAD) systems, in coordination with artificial intelligence (AI) techniques, have a vital role in the early detection of this disorder. In this review, we studied 124 research articles published from 2000 to 2022. Here, the challenges faced by CAD systems based on different modalities are highlighted along with the current requirements of this domain and future prospects in this area of research.
AB - A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range of health issues. As malignant brain tumors grow rapidly, the mortality rate of individuals with this cancer can increase substantially with each passing week. Hence it is vital to detect these tumors early so that preventive measures can be taken at the initial stages. Computer-aided diagnostic (CAD) systems, in coordination with artificial intelligence (AI) techniques, have a vital role in the early detection of this disorder. In this review, we studied 124 research articles published from 2000 to 2022. Here, the challenges faced by CAD systems based on different modalities are highlighted along with the current requirements of this domain and future prospects in this area of research.
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U2 - 10.1016/j.compbiomed.2023.107063
DO - 10.1016/j.compbiomed.2023.107063
M3 - Review article
AN - SCOPUS:85161957423
SN - 0010-4825
VL - 163
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 107063
ER -
