Accurate evaluation of blood cells plays a pivotal role in assessing immune system functionality and diagnosing various disorders. While complete blood counts (CBCs) can be obtained rapidly using cell counters, manual blood smear inspection remains crucial for patient monitoring and verification of results. However, this manual analysis is labor-intensive, time-consuming, and prone to errors. To overcome these challenges, we propose an automated solution combining advanced image-processing techniques and deep learning algorithms to automatically recognize complete blood cells in blood smear images. This study introduces an enhanced CNN method for automated blood cell identification and categorization, addressing the challenges associated with manual blood smear inspection in patient monitoring and result verification. The proposed solution combines advanced image-processing techniques and deep learning algorithms to automatically recognize complete blood cells in blood smear images. The approach involves preprocessing input images and harnessing the power of deep neural networks to enhance predictions and corrections. A novel method is also introduced, effectively utilizing the interaction between model outcomes and geographical information to improve classification quality. The proposed model achieves impressive metrics through extensive training and validation, including 90.18 % accuracy, 91 % precision, 90 % recall, and 88 % F-score, surpassing conventional Computer-Aided Diagnosis (CAD) systems in clinical laboratories. The utilization of independent creation and evaluation of neural algorithms with the Adam optimizer enhances the robustness and reliability of the proposed method. The results demonstrate the potential of the approach in clinical and diagnostic labs, offering accurate and efficient automated CBC recognition. The study outlines the plan to integrate the model with a blockchain-based blood supply management system. This integration aims to ensure a secure and efficient delivery of high-quality blood during transfusions, enhancing patient safety and care. By relieving hematologists from labor-intensive and time-consuming tasks, the technology presented in this study paves the way for a future where precision, excellence, and optimal patient outcomes converge seamlessly in the field of blood cell identification and categorization.

Ensuring data security and privacy has emerged as a serious concern in the realm of blood supply chain. This is mainly because of sensitivity of donor information, the involvement of multiple stakeholders, and the need for transparent traceability. This paper proposes a novel privacy-preserving, permissioned blockchain framework for blood supply chain management that integrates Hyperledger Fabric, the InterPlanetary File System (IPFS), and a Zero-Knowledge Proof (ZKP)-based authentication protocol. The framework introduces a Pseudonymous Role-Bound Zero-Knowledge Authentication (PRZKA) mechanism that enables donors to authenticate and authorize access to their medical data without revealing their real identities. Context-specific pseudonyms derived through cryptographic hash-to-curve operations ensure unlinkability across different healthcare interactions, while Schnorr-style challenge–response proofs prevent replay attacks and credential misuse. Sensitive donor information is protected using Fabric Private Data Collections, whereas encrypted medical records are stored off-chain in IPFS, with only secure content identifiers recorded on the blockchain. Smart contracts enforce fine-grained, consent-aware access control policies and maintain immutable audit logs of all access events. The proposed system architecture combines an off-chain ZKP gateway with on-chain authorization logic to minimize blockchain overhead while preserving strong security guarantees. Furthermore, a performance evaluation framework is defined, including metrics, workload scenarios, and system configurations, to support future empirical validation. Security analysis indicates that the proposed framework enhances privacy, prevents identity linkage, and enables auditable, consent-driven data sharing compared with existing blockchain-based healthcare solutions.