CLASSIFICATION OF LIVER CANCER VIA DEEP LEARNING BASED DILATED ATTENTION CONVOLUTIONAL NEURAL NETWORK
Journal Title: International Journal of Data Science and Artificial Intelligence - Year 2024, Vol 2, Issue 04
Abstract
Liver cancer occur when normal cells develop aberrant DNA alterations and reproduce uncontrollably. Patients with cirrhosis, hepatitis B or C, or both have an increased risk of developing the progressing stage of cancer. The radiologists spend more time for detecting the liver cancer when analysing with traditional methods. Early detection of liver cancer can help doctors and radiation therapists identify the tumours. However, manual identification of liver cancer is time-intensive and challenging process in the current scenario. In this work, an automated deep learning network is designed to classify the liver cancer in its initial phase. At first, the CT scans are gathered from the publicly available LiTS database and these gathered images are pre-processed using Gaussian filter is used for reducing the noises and to smoothen the edges. The liver region is segmented using Enhanced otsu (EM) method is utilized to segment the liver region separately from the pre-processed input images. Afterwards, Dilated Convolutional Neural Network (DCNN) with the attention block is employed for classifying the liver cancer into tri-classes such as normal controls (NC), hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC) cases based on the extracted features. The efficiency of the proposed DA-CNN is evaluated using the attributes viz., accuracy, sensitivity, precision, specificity, and F1-score values are computed as classification results. The experimental fallouts disclose that the DA-CNN attains an accuracy range of 98.20%. Moreover, the proposed DA-CNN advances the overall accuracy by 3.25%, 5.29%, and 0.99% better than Optimised GAN, OPBS-SSHC, HFCNN respectively.
Authors and Affiliations
R. Ramani, K. Vimala Devi, P. Thiruselvan and M. Umamaheswari
Keywords
Related Articles
- EP ID EP744943
- DOI -
- Views 19
- Downloads 0
SAFE-ACID: A NOVEL SECURITY ASSESSMENT FRAMEWORK FOR IOT INTRUSION DETECTION VIA DEEP LEARNING
Internet of Things (IoT) intrusion detection is crucial for ensuring the security of interconnected devices in our digital world. With diverse devices communicating in complex networks, IoT environments face vulnerabilit...
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...
HYBRID OPTIMIZATION INTEGRATED INTRUSION DETECTION SYSTEM IN WSN USING ELMAN NETWORK
Wireless Sensor Networks (WSNs) increases the usage of integrated systems and areas which attracts the attention of attackers. However, WSNs are vulnerable to different kinds of security threats and attacks. To ensure th...
C-AVPSO: DYNAMIC LOAD BALANCING USING AFRICAN VULTURE PARTICLE SWARM OPTIMIZATION
Cloud computing is a novel technology that allows consumers to access services from anywhere, at any time, under different conditions, and is controlled by a third-party cloud provider. Cloud task scheduling is a complic...
DINGO OPTIMIZED FUZZY CNN TECHNIQUE FOR EFFICIENT PROTEIN STRUCTURE PREDICTION
Protein is made up of a variety of molecules that are required by living organisms, such as enzymes, hormones, and antibodies. In step 2, the max-pooling layer and the convolutional layer evaluate the input data to creat...