<p>The medical industry faces many security-related issues, especially regarding the protection of sensitive patient information contained in medical images transmitted over public networks. Existing encryption methods are often ineffective in handling problems related to capacity, redundancy, and data volume during transmission. To address these problems, this research proposes a Blockchain Elliptic Curve Cryptography-based Running City Game Search (BEC-RCS) method for securing medical images. The method contains preprocessing techniques such as resizing, normalization, and data augmentation to improve the quality of the input data for training. The model is applied to medical image security, in which the continuous security of medical images and the avoidance of various vulnerabilities and threats are guaranteed. The Elliptic Curve Cryptography is applied in the model to guarantee the confidentiality of lung images by encrypting and decrypting the medical images during transmission. The model also combines blockchain and Elliptic Curve Cryptography (ECC) to ensure the integrity and authenticity of the images. The hyperparameters of the model are optimized by using the Running City Game Search (RCS) algorithm, thereby effectively searching the space with high convergence. The method used different datasets, including Medical Image Segmentation (Hi-gMISnet), Medical MNIST, Medical Image dataset: Brain Tumor Detection, NIH Chest X-rays, and Chest CT-Scan Images datasets. The experimental outcomes show that the BEC-RCS approach reaches a Mean Square Error (MSE) of 0.20 and a Structured Similarity Index Measurement (SSIM) of 97.3%, which indicates better image preservation and security compared to existing encryption methods. These outcomes demonstrate that the BEC-RCS method is effective and provides the best medical image protection in the use of healthcare systems.</p>