HYBRIDIZATION OF DILATED CNN WITH ATTENTION LINK NET FOR BRAIN CANCER CLASSIFICATION
Journal Title: International Journal of Data Science and Artificial Intelligence - Year 2024, Vol 2, Issue 02
Abstract
Brain tumours (BT) are highly prevalent and dangerous disease, have extremely short prognoses at the most malignant grade. Merging the MRI modes results in hybrid images with information that is used to classify tumours. The obtained images are luxurious to gain and hard to store, the diagnostic process consume a substantial amount of time. The magnetic resonance imaging (MRI) provides clean structural data, but it is time-consuming. To overcome these issues, a novel deep learning-based model is proposed for the early detection and classification of brain tumour from MRI images. Initially, the input images are pre-processed utilizing Clifford gradient to improve the quality of the image. Then, hybrid dilated CNN model (HD-CNN) is employed for extracting the features in the pre-processed brain image. Afterward, the extracted features are fed into the Attention Link Net model to classify the four cases of brain tumour. According to the test result, the proposed model has a 99.23% accuracy rate. The Attention Link Net high accuracy than Alex Net, Dense Net, and Res Net which obtains 0.15%,0.31%, and 0.25% while having a significantly lower computational cost than other networks. The proposed model improves overall accuracy by 2.35%,0.94%, and 2.42%, over the VGG19, DNN, and DCNN, respectively.
Authors and Affiliations
Cheepurupalli Raghuram, V. S. R. K. Raju Dandu, B. Jaison
IOT-CENTRIC DATA PROTECTION USING DEEP LEARNING TECHNIQUE FOR PRESERVING SECURITY AND PRIVACY IN CLOUD
The Internet of Things (IoT) describes a system where interconnected physical objects are connected online. As the collection and sharing of vast amounts of personal data grow, so do concerns over user privacy within IoT...
YOLO-VEHICLE: REALTIME VEHICLE LICENCE PLATE DETECTION AND CHARACTER RECOGNITION USING YOLOV7 NETWORK
The demand for a secure lifestyle and travel is increasing due to the rapid development of technology. Since the turn of the century, the number of road vehicles has risen dramatically. The rapid growth of the vehicular...
A NOVEL INTERNET OF THINGS-BASED ELECTROCARDIOGRAM DENOISING METHOD USING MEDIAN MODIFIED WEINER AND EXTENDED KALMAN FILTERS
The Internet of Things (IoT) offers healthcare applications that benefit customers, physicians, hospitals, and insurance companies. Wearable technology like fitness bands and other wirelessly connected gadgets like blood...
REAL TIME MASKED FACE RECOGNITION USING DEEP LEARNING BASED YOLOV4 NETWORK
A global outbreak of COVID-19 has been spreading rapidly since 2019. This pandemic is making human existence more complex and intricate and thousands have been killed by this disease. A lack of antiviral medications is o...
CHICKEN SWARM OPTIMIZATION BASED ENSEMBLED LEARNING CLASSIFIER FOR BLACK HOLE ATTACK IN WIRELESS SENSOR NETWORK
Wireless Sensor Networks (WSNs) are an inevitable technology prevalently used in various critical and remote monitoring applications. The security of WSNs is compromised by various attacks in wireless mediums. Even thoug...