Optimizing Gastric Cancer Classification with QCNN and Fine-Tuning

Abstract

Cancer ranks as one of the primary contributors to morbidity and mortality worldwide, standing as the second leading causeof death on a global scale. According to data from the National Cancer Registry Program of the Indian Council of Medical Research, over1300 individuals in India lose their lives daily as a result of cancer-related causes. Gastric cancer is among the top five most prevalent cancersglobally, after cancer in the lung, breast, colorectum, and prostate, highlighting the importance of accurate classification for effectivetreatment strategies. In this study, a novel approach utilizing a Quadratic Convolutional Neural Network combined with Extreme Learningand Fine-Tuning technique, a deep learning architecture specifically designed to capture intricate patterns and features within medicalimaging data. Fine tuning technique is used to enhance the model's generalization capability and adaptability to diverse datasets. Throughextensive experimentation and validation on a comprehensive dataset comprising gastric cancer images, the proposed approach achievesan impressive accuracy of 94%. The findings indicate the efficacy of the proposed approach for classifying gastric cancer. With its highaccuracy and robust performance, the developed QCNN model holds promise for assisting clinicians in accurate diagnosis and prognosis ofgastric cancer patients, ultimately contributing to improved patient outcomes and personalized treatment strategies.