IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning

Siddiqui, Shahan Yamin; Haider, Amir; Ghazal, Taher M.; Khan, Muhammad Adnan; Naseer, Iftikhar; Abbas, Sagheer; Rahman, MuhibUr; Khan, Junaid; Ahmad, Munir; Hasan, Mohammad Kamrul; A, Afifi Mohammed.; Ateeq, Karamath

doi:10.1109/access.2021.3123472

Cited by 143 publications

(54 citation statements)

References 27 publications

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“…Some researchers provide the fused machine learning-based solution [ 30 ] to predict cancer using real-time data and achieved good accuracy using different neural algorithms. Previous research used cloud-based deep learning approaches [ 31 ] to overcome cancerous diseases and gave better treatment to decrease the high mortality rate in females. Machine learning approaches help in genes association to overcome the cancerous empowered with deep learning approaches [ 32 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

Rahman

Alqahtani

Aldhafferi

et al. 2022

Sensors

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Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

Rahman

Alqahtani

Aldhafferi

et al. 2022

Sensors

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“…But the model did not show the advantages of segmenting tasks. Based (25), breast cancer detection (26), and cervical cancer classification (27).…”

Section: Research Status Of Deep Learningmentioning

confidence: 99%

“…Lin et al ( 24 ) developed a deep learning model for COVID-19 identification and compared the CT images of TD and the coronavirus. Deep learning methods are also widely used in face recognition ( 25 ), breast cancer detection ( 26 ), and cervical cancer classification ( 27 ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Tracheobronchial Diverticula Based on Semantic Segmentation of CT Images via the Dual-Channel Attention Network

Zhang

Ding²,

Guo

2022

Front. Public Health

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Tracheobronchial diverticula (TD) is a common cystic lesion that can be easily neglected; hence accurate and rapid identification is critical for later diagnosis. There is a strong need to automate this diagnostic process because traditional manual observations are time-consuming and laborious. However, most studies have only focused on the case report or listed the relationship between the disease and other physiological indicators, but a few have adopted advanced technologies such as deep learning for automated identification and diagnosis. To fill this gap, this study interpreted TD recognition as semantic segmentation and proposed a novel attention-based network for TD semantic segmentation. Since the area of TD lesion is small and similar to surrounding organs, we designed the atrous spatial pyramid pooling (ASPP) and attention mechanisms, which can efficiently complete the segmentation of TD with robust results. The proposed attention model can selectively gather features from different branches according to the amount of information they contain. Besides, to the best of our knowledge, no public research data is available yet. For efficient network training, we constructed a data set containing 218 TD and related ground truth (GT). We evaluated different models based on the proposed data set, among which the highest MIOU can reach 0.92. The experiments show that our model can outperform state-of-the-art methods, indicating that the deep learning method has great potential for TD recognition.

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“…The healthcare sector is facing different challenges that require the application of technology to enable it to work effectively [20]. Developing accurate technologies for the purposes of healthcare improvement has faced challenges because there are many needs that have to be satisfied [21]. Developing an IT-based solution for all these needs can be challenging.…”

Section: Introductionmentioning

confidence: 99%

Challenges Facing the Application of IoT in Medicine and Healthcare

Khan

2021

Int. J. CIM

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Among all the functions of the Internet of Things, healthcare applications are the most important. The internet of Things has played a vital role in the sharing of various medical resources making it an essential factor in the field of medicine. Without the applications, it may become challenging for the medical practice to achieve some objectives. Today, information is shared freely across different networks, making it easy for practitioners and institutions to work with the available resources and deliver on the medical needs of the society. IoT has made it possible to offer smart and effective healthcare services to people. The application of smart sensors is one of the factors that has contributed to effective management of healthcare needs in the society. Wearable devices can be used to track different issues within the body. Some can be embedded to monitor different functions of the body to ensure there is effectiveness in the way medical services are delivered to such patients. The information collected in such manner, can be analyzed, aggregated and mined to do the early prediction of diseases. Processing algorithms play a vital role in helping practitioners in developing personalized treatment, which makes healthcare more economical. Apart from analyzing the effectiveness of IoT in healthcare, challenges will be discussed in this study and how they impact medicine.

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IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning

Cited by 143 publications

References 27 publications

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

Analysis of Tracheobronchial Diverticula Based on Semantic Segmentation of CT Images via the Dual-Channel Attention Network

Challenges Facing the Application of IoT in Medicine and Healthcare

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