Revolutionizing Esophageal Cancer Diagnosis: A Deep Learning-Based Method in Endoscopic Images

Esophageal cancer (EC) is a severe and commonly increasing disease due to the uncontrolled growth in the esophagus. It is the sixth leading cause of cancer-related deaths worldwide. The traditional methods for the diagnosis of EC are not only time-consuming but also suffer from inconsistencies due to human factors such as experience and fatigue. This paper proposes a deep learning (DL) approach for the detection of EC from endoscopic images to improve efficiency and accuracy. The study utilizes an endoscopic image dataset of 2000 images evenly split into cancerous and non-cancerous cases. After image preprocessing and augmentation, these images are fed into the proposed Inception ResNet V2 model. The extracted features were processed by the final classification layers and produced class probabilities. The simulation results revealed that the suggested model attained 98.50% of accuracy, 97.50% of precision, 98.75% of recall and 98.00% of F1 score after finetuning. These results underscore the model's capability to accurately identify EC, minimizing false positives and enhancing diagnostic reliability. The proposed DL framework for automated EC detection, promising advancements in clinical workflows and patient care.