IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing

Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma devel...

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Main Authors: Muhammad Umar Nasir, Safiullah Khan, Shahid Mehmood, Muhammad Adnan Khan, Atta-ur Rahman, Seong Oun Hwang
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/22/14/5444
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author Muhammad Umar Nasir
Safiullah Khan
Shahid Mehmood
Muhammad Adnan Khan
Atta-ur Rahman
Seong Oun Hwang
author_facet Muhammad Umar Nasir
Safiullah Khan
Shahid Mehmood
Muhammad Adnan Khan
Atta-ur Rahman
Seong Oun Hwang
author_sort Muhammad Umar Nasir
collection DOAJ
description Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma’s manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency.
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spelling doaj.art-1b830b7c30d54137bad4faf652d69efa2023-12-01T22:41:08ZengMDPI AGSensors1424-82202022-07-012214544410.3390/s22145444IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge ComputingMuhammad Umar Nasir0Safiullah Khan1Shahid Mehmood2Muhammad Adnan Khan3Atta-ur Rahman4Seong Oun Hwang5Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore 54000, PakistanDepartment of IT Convergence Engineering, Gachon University, Seongnam 13120, KoreaRiphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore 54000, PakistanPattern Recognition and Machine Learning Lab., Department of Software, Gachon University, Seongnam 13120, KoreaDepartment of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Computer Engineering, Gachon University, Seongnam 13120, KoreaBone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma’s manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency.https://www.mdpi.com/1424-8220/22/14/5444blockchainfog computingedge computingosteosarcoma cancertransfer learningIoMT
spellingShingle Muhammad Umar Nasir
Safiullah Khan
Shahid Mehmood
Muhammad Adnan Khan
Atta-ur Rahman
Seong Oun Hwang
IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing
Sensors
blockchain
fog computing
edge computing
osteosarcoma cancer
transfer learning
IoMT
title IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing
title_full IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing
title_fullStr IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing
title_full_unstemmed IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing
title_short IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing
title_sort iomt based osteosarcoma cancer detection in histopathology images using transfer learning empowered with blockchain fog computing and edge computing
topic blockchain
fog computing
edge computing
osteosarcoma cancer
transfer learning
IoMT
url https://www.mdpi.com/1424-8220/22/14/5444
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