FL-QNNs: Memory Efficient and Privacy Preserving Framework for Peripheral Blood Cell Classification
Human blood predominantly consists of plasma, erythrocytes, leukocytes, and thrombocytes. It is crucial for the transportation of oxygen and nutrients and for storing the health information of the human body. The body uses blood cells to fight against diseases and infections. Consequently, blood ana...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11112772/ |
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| author | Meenakshi Aggarwal Vikas Khullar Nitin Goyal Bhavani Sankar Panda Hardik Doshi Nafeesh Ahmad Vivek Bhardwaj Gaurav Sharma |
| author_facet | Meenakshi Aggarwal Vikas Khullar Nitin Goyal Bhavani Sankar Panda Hardik Doshi Nafeesh Ahmad Vivek Bhardwaj Gaurav Sharma |
| author_sort | Meenakshi Aggarwal |
| collection | DOAJ |
| description | Human blood predominantly consists of plasma, erythrocytes, leukocytes, and thrombocytes. It is crucial for the transportation of oxygen and nutrients and for storing the health information of the human body. The body uses blood cells to fight against diseases and infections. Consequently, blood analysis enables clinicians to evaluate a person’s physiological state. These blood cells are categorized into eight classes such as neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes, erythroblasts, and platelets based on nuclear characteristics, morphology, and cytoplasmic composition. Historically, pathologists and hematologists have discovered and analyzed specimens using microscopy before physical categorization, a method that is time consuming and susceptible to human error. Consequently, automating this procedure is imperative. This study proposes a resource efficient, privacy preserving, optimized memory framework by incorporating two approaches: Federated learning and quantized neural network (FL-QNNs) for peripheral blood cell (PBC) image classification. The data set include the total 17,092 images of eight classes. The study began by training the model with seven Deep learning Convolutional neural network (DL-CNN) model such as LeNet, SimpleCNN, MiniAlexNet, SmallVGG, TinyResNet, MiniMobileNet, and Multiple layer perceptron (MLP) and consider them as a baseline models achieving a 98-99% accuracy. Then implement the federated learning (FL) framework with IID (Independent and identically distributed) and Non-IID datasets maintain similar results by preserving data. Further FL-QNNs approach is used with these baseline models and achieved 98-99% accuracy and while optimizing memory, preserving data and resources. This makes the technique exceptionally appropriate for implementation in real-world, privacy-sensitive, and resource-limited healthcare settings. FL-QNN is recognized as an effective approach for minimizing substantial computational resources and memory associated with several Deep Learning (DL) methods. |
| format | Article |
| id | doaj-art-a64f3616a9104b5087d0a0726fb0c3d5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a64f3616a9104b5087d0a0726fb0c3d52025-08-20T03:40:44ZengIEEEIEEE Access2169-35362025-01-011313819713821410.1109/ACCESS.2025.359586111112772FL-QNNs: Memory Efficient and Privacy Preserving Framework for Peripheral Blood Cell ClassificationMeenakshi Aggarwal0Vikas Khullar1https://orcid.org/0000-0002-0404-3652Nitin Goyal2Bhavani Sankar Panda3Hardik Doshi4Nafeesh Ahmad5Vivek Bhardwaj6https://orcid.org/0000-0002-2288-6987Gaurav Sharma7Bhagwan Parshuram Institute of Technology, Guru Gobind Singh Indraprastha University (GGSIPU), Rohini, New Delhi, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, IndiaDepartment of Computer Science and Engineering, School of Engineering and Technology, Central University of Haryana, Mahendragarh, Haryana, IndiaDepartment of Computer Science and Engineering, Raghu Engineering College, Visakhapatnam, Andhra Pradesh, IndiaMarwadi University Research Center, Department of Computer Engineering, Faculty of Engineering and Technology, Marwadi University, Rajkot, Gujarat, IndiaDepartment of Electronics and Communication Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, IndiaSchool of Computer Science and Engineering, Manipal University Jaipur, Jaipur, IndiaDepartment of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, Tamil Nadu, IndiaHuman blood predominantly consists of plasma, erythrocytes, leukocytes, and thrombocytes. It is crucial for the transportation of oxygen and nutrients and for storing the health information of the human body. The body uses blood cells to fight against diseases and infections. Consequently, blood analysis enables clinicians to evaluate a person’s physiological state. These blood cells are categorized into eight classes such as neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes, erythroblasts, and platelets based on nuclear characteristics, morphology, and cytoplasmic composition. Historically, pathologists and hematologists have discovered and analyzed specimens using microscopy before physical categorization, a method that is time consuming and susceptible to human error. Consequently, automating this procedure is imperative. This study proposes a resource efficient, privacy preserving, optimized memory framework by incorporating two approaches: Federated learning and quantized neural network (FL-QNNs) for peripheral blood cell (PBC) image classification. The data set include the total 17,092 images of eight classes. The study began by training the model with seven Deep learning Convolutional neural network (DL-CNN) model such as LeNet, SimpleCNN, MiniAlexNet, SmallVGG, TinyResNet, MiniMobileNet, and Multiple layer perceptron (MLP) and consider them as a baseline models achieving a 98-99% accuracy. Then implement the federated learning (FL) framework with IID (Independent and identically distributed) and Non-IID datasets maintain similar results by preserving data. Further FL-QNNs approach is used with these baseline models and achieved 98-99% accuracy and while optimizing memory, preserving data and resources. This makes the technique exceptionally appropriate for implementation in real-world, privacy-sensitive, and resource-limited healthcare settings. FL-QNN is recognized as an effective approach for minimizing substantial computational resources and memory associated with several Deep Learning (DL) methods.https://ieeexplore.ieee.org/document/11112772/Deep learningconvolutional neural networkfederated learningquantized neural networkIID and non-IIDperipheral blood cell |
| spellingShingle | Meenakshi Aggarwal Vikas Khullar Nitin Goyal Bhavani Sankar Panda Hardik Doshi Nafeesh Ahmad Vivek Bhardwaj Gaurav Sharma FL-QNNs: Memory Efficient and Privacy Preserving Framework for Peripheral Blood Cell Classification IEEE Access Deep learning convolutional neural network federated learning quantized neural network IID and non-IID peripheral blood cell |
| title | FL-QNNs: Memory Efficient and Privacy Preserving Framework for Peripheral Blood Cell Classification |
| title_full | FL-QNNs: Memory Efficient and Privacy Preserving Framework for Peripheral Blood Cell Classification |
| title_fullStr | FL-QNNs: Memory Efficient and Privacy Preserving Framework for Peripheral Blood Cell Classification |
| title_full_unstemmed | FL-QNNs: Memory Efficient and Privacy Preserving Framework for Peripheral Blood Cell Classification |
| title_short | FL-QNNs: Memory Efficient and Privacy Preserving Framework for Peripheral Blood Cell Classification |
| title_sort | fl qnns memory efficient and privacy preserving framework for peripheral blood cell classification |
| topic | Deep learning convolutional neural network federated learning quantized neural network IID and non-IID peripheral blood cell |
| url | https://ieeexplore.ieee.org/document/11112772/ |
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