A Secure Medical Image Processing Scheme for Detection of Pneumonia Using Transfer Learning

Medical image processing plays a crucial role in the diagnosis and treatment of diverse diseases. The advancements in deep learning and transfer learning techniques have greatly contributed to automated disease detection, providing valuable assistance to healthcare professionals. This paper presents a secure medical image processing scheme utilizing zero watermarking for the detection of pneumonia through transfer learning. Patients have the convenience of remotely sending their health records to doctors for disease diagnosis. The proposed scheme focuses on maintaining the integrity and confidentiality of medical images during transmission and storage. A comparative study is conducted to assess the quality of chest X-rays and embedded chest X-rays. Various convolutional neural network (CNN) models are utilized, and the best pre-trained CNN model is employed for feature extraction and transfer learning to enhance the accuracy of the classification model. Moreover, a zero watermarking technique is incorporated to embed patient information into medical images for authentication purposes. Experimental results demonstrate the high accuracy achieved by the proposed scheme in detecting pneumonia while ensuring the security of medical images. The versatility of the proposed system extends to various medical applications, including telemedicine, medical data sharing, and electronic health records.