Leveraging AI-Driven Machine Learning Approaches -SVM and RESNET-50 For Predicting Esophageal Cancer Detection

Esophageal cancer, a very aggressive malignancy, exhibits a significant mortality rate attributed to late diagnosis and diverse patient responses to therapeutic interventions including chemotherapy, radiation therapy, and immunotherapy. Customized therapy suggestions are essential for enhancing patient outcomes; however, existing methodologies depend on generic models that fail to consider individual heterogeneity. This paper proposes an AI-driven approach utilizing machine learning algorithms, that includes Support Vector Machines (SVM) ResNet-50 structure for deep convolutional neural networks and its objective is to classify the Barrett's esophagus and adenocarcinoma cancers. As multilayered perceptron's are the center of the approach, determined which maximal classification in ResNet-50 would be appropriate layers and trained them to cut to avoid redundancy. It was demonstrated that regional information and high-dimensional features are important to enhance classification. Also, maximal layer discrimination and differential effect can be important in Barrett's esophagus and adenocarcinoma classification training on ResNet50. The model improves precision through the incorporation of clinical data to plan individualized treatment pathways. It has shown more sensitivity in terms of the detection of neoplasia in Barrett's esophagus, reaching up to 90% as compared to conventional techniques. The experimental findings indicate enhanced prediction accuracy, precision, sensitivity relative to conventional approaches. This AI system offers a prospective option for the optimization of individualized treatment in esophageal cancer therapy.