Medical images play a crucial role in diagnosis, treatment procedures and overall healthcare. Nevertheless, they also pose substantial risks to patient confidentiality and safety. Safeguarding the confidentiality of patients' data has become an urgent and practical concern. We present a novel approach for reversible data hiding for colour medical images. In a hybrid domain, we employ AlexNet, tuned with watershed transform (WST) and L-shaped fractal Tromino encryption. Our approach commences by constructing the host image's feature vector using a pre-trained AlexNet model. Next, we use the watershed transform to convert the extracted feature vector into a vector for a topographic map, which we then encrypt using an L-shaped fractal Tromino cryptosystem. We embed the secret image in the transformed image vector using a histogram-based embedding strategy to enhance payload and visual fidelity. When there are no attacks, the RDHNet exhibits robust performance, can be reversed to the original image and maintains a visually appealing stego image, with an average PSNR of 73.14 dB, an SSIM of 0.9999 and perfect values of NC = 1 and BER = 0 under normal conditions. The proposed RDHNet demonstrates a robust ability to withstand detrimental geometric and noise-adding attacks as well as various steganalysis methods. Furthermore, our RDHNet method initiative demonstrates efficacy in tackling contemporary confidentiality issues.