Enhanced detection of leiomyosarcoma using opsit and LSTM-GRNN for improved medical image analysis
G. Petchinathan,Parveen Begam Abdul Kareem,Shanmugaraja P.,Anita Venaik,Selva Lakshmi C.B.
Abstract
Leiomyosarcoma is a rare type of cancer that spreads to various parts of the body to create an aggressive form of complex tissue sarcoma disease. Artificial Intelligence (AI) powered technologies play a vital role in screening medical images to identify sarcoma types of diseases for early diagnosis and treatment to avoid cancer risks. In the preliminary stages, the machine learning and deep learning models potentially impact the identification of cancer levels; due to the invasive point of image degradation and feature inconsistency levels, the precision level attains low accuracy, leading to higher false negatives. To solve these problems, to propose an Optimal Particle Swarm Intelligence Technique (OPSIT) for feature selection with Long Short-Term Memory Gated Recurrent Neural Network (LSTM-GRNN) to identify the disease effectively. Initially, a bilateral wavelet filter (BWM) is carried out preprocessing to normalize the feature scaling and improve the image scalar margins. Then, the Linear Reiterative Clustering (LRC) algorithm is applied to segment the non-invasive point of the disease scalar region to split the cancer cells. Further, to scale, the active disease margins in cancer cell features are evaluated with OPSIT to reduce the non-relation feature in the segmented image region. Finally, the LSTM-GRNN algorithm is applied to train the scaled entity of the cancer image region, with Actual threshold margins to identify the disease region accurately. The proposed system increases the proper positive actual scaling region of the cancer region to increase precision rate and attain high accuracy, sensitivity, specificity, and ROC performance compared to the other systems.
G. Petchinathan — Department of Biomedical Engineering, Sri Shanmugha College of Engineering and Technology, Pullipalayam, Morur, Sankari (Tk), Salem (Dt.)
Parveen Begam Abdul Kareem — Department of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
Shanmugaraja P. — Department of InformationTechnology, Sona College of Technology, Salem, Tamil Nadu India
Anita Venaik — Department of InformationTechnology, Amity Business School, Amity University, Noida, India
Selva Lakshmi C.B. — Department Computer Science and Engineering, Velammal College of Engineering and Technology, Madurai, Tamil Nadu, India
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