This companion paper focuses on the reproducibility of our previously accepted work on the proposed MeDiANet architecture, a lightweight computationally efficient architecture designed for medical image classification. We outline key practices, including consistent random seed initialization and mixed-precision training with TensorFlow. By detailing these essential steps, we provide a clear framework to enable other researchers to replicate the findings, ensuring transparency and reliability in deep learning experiments. The code is available at: https://github.com/dipayandewan94/MeDiANet .