Utilizing Deep Feature Fusion for Automatic Leukemia Classification: An Internet of Medical Things-Enabled Deep Learning Framework

被引:1
|
作者
Islam, Md Manowarul [1 ]
Rifat, Habibur Rahman [1 ]
Shahid, Md. Shamim Bin [1 ]
Akhter, Arnisha [1 ]
Uddin, Md Ashraf [2 ]
机构
[1] Jagannath Univ, Dept Comp Sci & Engn, Dhaka 1100, Bangladesh
[2] Deakin Univ, Sch Info Technol, Burwood, Vic 3125, Australia
关键词
leukemia; VGG16; DenseNet-121; segmentation; feature fusion; transfer learning; internet of medical things; DIAGNOSIS;
D O I
10.3390/s24134420
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Acute lymphoblastic leukemia, commonly referred to as ALL, is a type of cancer that can affect both the blood and the bone marrow. The process of diagnosis is a difficult one since it often calls for specialist testing, such as blood tests, bone marrow aspiration, and biopsy, all of which are highly time-consuming and expensive. It is essential to obtain an early diagnosis of ALL in order to start therapy in a timely and suitable manner. In recent medical diagnostics, substantial progress has been achieved through the integration of artificial intelligence (AI) and Internet of Things (IoT) devices. Our proposal introduces a new AI-based Internet of Medical Things (IoMT) framework designed to automatically identify leukemia from peripheral blood smear (PBS) images. In this study, we present a novel deep learning-based fusion model to detect ALL types of leukemia. The system seamlessly delivers the diagnostic reports to the centralized database, inclusive of patient-specific devices. After collecting blood samples from the hospital, the PBS images are transmitted to the cloud server through a WiFi-enabled microscopic device. In the cloud server, a new fusion model that is capable of classifying ALL from PBS images is configured. The fusion model is trained using a dataset including 6512 original and segmented images from 89 individuals. Two input channels are used for the purpose of feature extraction in the fusion model. These channels include both the original and the segmented images. VGG16 is responsible for extracting features from the original images, whereas DenseNet-121 is responsible for extracting features from the segmented images. The two output features are merged together, and dense layers are used for the categorization of leukemia. The fusion model that has been suggested obtains an accuracy of 99.89%, a precision of 99.80%, and a recall of 99.72%, which places it in an excellent position for the categorization of leukemia. The proposed model outperformed several state-of-the-art Convolutional Neural Network (CNN) models in terms of performance. Consequently, this proposed model has the potential to save lives and effort. For a more comprehensive simulation of the entire methodology, a web application (Beta Version) has been developed in this study. This application is designed to determine the presence or absence of leukemia in individuals. The findings of this study hold significant potential for application in biomedical research, particularly in enhancing the accuracy of computer-aided leukemia detection.
引用
收藏
页数:23
相关论文
共 50 条
  • [1] Agriculture monitoring system based on internet of things by deep learning feature fusion with classification
    Kumari, K. Sita
    Haleem, S. L. Abdul
    Shivaprakash, G.
    Saravanan, M.
    Arunsundar, B.
    Pandraju, Thandava Krishna Sai
    COMPUTERS & ELECTRICAL ENGINEERING, 2022, 102
  • [2] Internet of things-enabled real-time health monitoring system using deep learning
    Xingdong Wu
    Chao Liu
    Lijun Wang
    Muhammad Bilal
    Neural Computing and Applications, 2023, 35 : 14565 - 14576
  • [3] Internet of things-enabled real-time health monitoring system using deep learning
    Wu, Xingdong
    Liu, Chao
    Wang, Lijun
    Bilal, Muhammad
    NEURAL COMPUTING & APPLICATIONS, 2023, 35 (20): : 14565 - 14576
  • [4] Deep Learning Enabled Disease Diagnosis for Secure Internet of Medical Things
    Ahmad, Sultan
    Khan, Shakir
    AlAjmi, Mohamed Fahad
    Dutta, Ashit Kumar
    Dang, L. Minh
    Joshi, Gyanendra Prasad
    Moon, Hyeonjoon
    CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 73 (01): : 965 - 979
  • [5] Deep Learning Enabled Disease Diagnosis for Secure Internet of Medical Things
    Ahmad, Sultan
    Khan, Shakir
    AlAjmi, Mohamed Fahad
    Dutta, Ashit Kumar
    Dang, L. Minh
    Joshi, Gyanendra Prasad
    Moon, Hyeonjoon
    Computers, Materials and Continua, 2022, 73 (01): : 965 - 979
  • [6] Human centric attention with deep multiscale feature fusion framework for activity recognition in Internet of Medical Things
    Hussain, Altaf
    Khan, Samee Ullah
    Rida, Imad
    Khan, Noman
    Baik, Sung Wook
    INFORMATION FUSION, 2024, 106
  • [7] A lightweight deep learning architecture for automatic modulation classification of wireless internet of things
    Han, Jia
    Yu, Zhiyong
    Yang, Jian
    IET COMMUNICATIONS, 2024, 18 (18) : 1220 - 1230
  • [8] Optimal Feature Selection-Based Medical Image Classification Using Deep Learning Model in Internet of Medical Things
    Raj, R. Joshua Samuel
    Shobana, S. Jeya
    Pustokhina, Irina Valeryevna
    Pustokhin, Denis Alexandrovich
    Gupta, Deepak
    Shankar, K.
    IEEE ACCESS, 2020, 8 : 58006 - 58017
  • [9] An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment
    Jameel, Syed Muslim
    Hashmani, Manzoor Ahmed
    Rehman, Mobashar
    Budiman, Arif
    SENSORS, 2020, 20 (20) : 1 - 25
  • [10] Deep Learning Framework for Analysis of Health Factors in Internet-of-Medical Things
    Abbas S.H.
    Kolikipogu R.
    Reddy V.L.
    Maroor J.P.
    Kumar D.
    Singh M.
    Radioelectronics and Communications Systems, 2023, 66 (03) : 146 - 154