Implementation of Lung Cancer Diagnosis based on DNN in Healthcare System

Zeki, Suhiar Mohammed

doi:10.14704/web/v18si04/web18166

Cited by 4 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Patients’ data are collected via health records (HRs), IoT sensor systems, personal and mobile phones, internet data, and social networks, which are emphasized among recent innovations. The PH uses artificial intelligence (AI) approaches to enhance illness development, illness prognosis, self-regulation and the self, and clinical intervention procedures using the set of data generated [ 19 ]. Recent studies are progressively looking into IoT devices in the healthcare profession.…”

Section: Primary Knowledgementioning

confidence: 99%

Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer

Ali

Chooralil²,

Balasubramanian

et al. 2023

Bioengineering

View full text Add to dashboard Cite

Recently, deep learning and the Internet of Things (IoT) have been widely used in the healthcare monitoring system for decision making. Disease prediction is one of the emerging applications in current practices. In the method described in this paper, lung cancer prediction is implemented using deep learning and IoT, which is a challenging task in computer-aided diagnosis (CAD). Because lung cancer is a dangerous medical disease that must be identified at a higher detection rate, disease-related information is obtained from IoT medical devices and transmitted to the server. The medical data are then processed and classified into two categories, benign and malignant, using a multi-layer CNN (ML-CNN) model. In addition, a particle swarm optimization method is used to improve the learning ability (loss and accuracy). This step uses medical data (CT scan and sensor information) based on the Internet of Medical Things (IoMT). For this purpose, sensor information and image information from IoMT devices and sensors are gathered, and then classification actions are taken. The performance of the proposed technique is compared with well-known existing methods, such as the Support Vector Machine (SVM), probabilistic neural network (PNN), and conventional CNN, in terms of accuracy, precision, sensitivity, specificity, F-score, and computation time. For this purpose, two lung datasets were tested to evaluate the performance: Lung Image Database Consortium (LIDC) and Linear Imaging and Self-Scanning Sensor (LISS) datasets. Compared to alternative methods, the trial outcomes showed that the suggested technique has the potential to help the radiologist make an accurate and efficient early lung cancer diagnosis. The performance of the proposed ML-CNN was analyzed using Python, where the accuracy (2.5–10.5%) was high when compared to the number of instances, precision (2.3–9.5%) was high when compared to the number of instances, sensitivity (2.4–12.5%) was high when compared to several instances, the F-score (2–30%) was high when compared to the number of cases, the error rate (0.7–11.5%) was low compared to the number of cases, and the computation time (170 ms to 400 ms) was low compared to how many cases were computed for the proposed work, including previous known methods. The proposed ML-CNN architecture shows that this technique outperforms previous works.

show abstract

Section: Primary Knowledgementioning

confidence: 99%

Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer

Ali

Chooralil²,

Balasubramanian

et al. 2023

Bioengineering

View full text Add to dashboard Cite

show abstract

“…Hospital staff, nurses, doctors, and clinical facilities are critical in diagnosing this pandemic [9]. Many strategies were implemented so as to mitigate effects of COVID-19.…”

Section: Figure 1 Type Of Datasetmentioning

confidence: 99%

A Survey of Deep Learning Applications for COVID-19 Detection Techniques Based on Medical Images

Saeed¹,

Oleiwi²

2022

ISI

View full text Add to dashboard Cite

The outbreak of the new coronavirus (COVID-19) has created a disaster worldwide and it became a very severe and acute disease. COVID-19 prevalence is rapidly increasing around the world. Deep learning (DL) technology had become a hot topic in the context of computing and is widely applied in various medical applications. These techniques have proven to be one of the effective tools for clinicians in the automatic diagnoses of COVID-19. The goal of the present paper is to provide an overview of recently developed systems based on DL techniques that use various medical imaging modalities such as Computer Tomography (CT) and Chest X-Rays (CXR). This review focuses on systems that had been developed for the diagnosis of COVID-19 with the use of the DL methods, as well as the well-known datasets that are utilized for the training of those networks. Finally, the researcher reviewed 58 research papers based on different medical images. Overall, this article aims to assist experts (medical or otherwise) and technicians to understand how the DL approaches are utilized in this context and the way that they can potentially be expanded to combat COVID-19 outbreaks.

show abstract

Unravelling the Mysteries of Lung Cancer: Harnessing the Power of Deep Learning for Detection and Classification

Dhayalini,

Ponmozhi

2024

2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intell

View full text Add to dashboard Cite

Implementation of Lung Cancer Diagnosis based on DNN in Healthcare System

Cited by 4 publications

References 0 publications

Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer

Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer

A Survey of Deep Learning Applications for COVID-19 Detection Techniques Based on Medical Images

Unravelling the Mysteries of Lung Cancer: Harnessing the Power of Deep Learning for Detection and Classification

Contact Info

Product

Resources

About