+1 Recommend
2 collections
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning


      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


          For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients' lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.

          Related collections

          Most cited references 48

          • Record: found
          • Abstract: found
          • Article: not found

          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
            • Record: found
            • Abstract: not found
            • Article: not found

            Densely Connected Convolutional Networks

              • Record: found
              • Abstract: not found
              • Article: not found

              A novel deep learning based framework for the detection and classification of breast cancer using transfer learning


                Author and article information

                J Healthc Eng
                J Healthc Eng
                Journal of Healthcare Engineering
                3 December 2020
                : 2020
                1Department of Information Technology, TheUniversity of Haripur, Haripur 22620, Pakistan
                2Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia
                3Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum-Beijing, Beijing 102249, China
                Author notes

                Academic Editor: Shah Nazir

                Copyright © 2020 Nighat Bibi et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Funded by: King Saud University
                Award ID: RSP-2020/184
                Funded by: Fundamental Research Funds for Central Universities
                Award ID: 2462020YJRC001
                Research Article


                Comment on this article