Deep learning application trial to lung cancer diagnosis for medical sensor systems

Shimizu, Ryo; Yanagawa, Shusuke; Monde, Yasutaka; Yamagishi, H.; Hamada, Mototsugu; Shimizu, Toru; Kuroda, Tadahiro

doi:10.1109/isocc.2016.7799852

Cited by 18 publications

(5 citation statements)

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“…Gas chromatography-mass spectrometry is a widely used method for this analysis. Many works demonstrate the effectiveness of this method in diagnosing tumor pathology [5][6][7][8][9][10][11][12]. However, it has some disadvantages, e.g., implementation requires highly qualified personnel, the method does not have sufficient mobility and it requires relatively expensive equipment [13,14].…”

Section: Introductionmentioning

confidence: 99%

Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors

et al. 2020

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“Electronic nose” technology, including technical and software tools to analyze gas mixtures, is promising regarding the diagnosis of malignant neoplasms. This paper presents the research results of breath samples analysis from 59 people, including patients with a confirmed diagnosis of respiratory tract cancer. The research was carried out using a gas analytical system including a sampling device with 14 metal oxide sensors and a computer for data analysis. After digitization and preprocessing, the data were analyzed by a neural network with perceptron architecture. As a result, the accuracy of determining oncological disease was 81.85%, the sensitivity was 90.73%, and the specificity was 61.39%.

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Section: Introductionmentioning

confidence: 99%

Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors

et al. 2020

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“…In Reference 15, Ryota Shimizu and Shusuke Yanagawa attempted to apply deep Learning to human urine information and accomplished 90% exactness in the assurance of lung cancer. This work demonstrated that deep learning is likewise powerful for human essential information investigation and can do pre‐diagnosis with no unique medicinal information.…”

Section: Related Workmentioning

confidence: 99%

Morphological feature extraction and KNG‐CNN classification of CT images for early lung cancer detection

Jena

George

2020

Int J Imaging Syst Tech

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Lung cancer is a dangerous disease causing death to individuals. Currently precise classification and differential diagnosis of lung cancer is essential with the stability and accuracy of cancer identification is challenging. Classification scheme was developed for lung cancer in CT images by Kernel based Non‐Gaussian Convolutional Neural Network (KNG‐CNN). KNG‐CNN comprises of three convolutional, two fully connected and three pooling layers. Kernel based Non‐Gaussian computation is used for the diagnosis of false positive or error encountered in the work. Initially Lung Image Database Consortium image collection (LIDC‐IDRI) dataset is used for input images and a ROI based segmentation using efficient CLAHE technique is carried as preprocessing steps, enhancing images for better feature extraction. Morphological features are extracted after the segmentation process. Finally, KNG‐CNN method is used for effectual classification of tumour > 30mm. An accuracy of 87.3% was obtained using this technique. This method is effectual for classifying the lung cancer from the CT scanned image.

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“…The proposed optimized CNN can reach minimal complexity as a result of the primary structural view of the CNN [ 13 , 14 ]. Consideration of IoT Sensor and Medical Images: A combination of sensor information and medical images is used to diagnose lung cancer from pictures [ 15 , 16 ]. …”

Section: Introductionmentioning

confidence: 99%

“…Consideration of IoT Sensor and Medical Images: A combination of sensor information and medical images is used to diagnose lung cancer from pictures [ 15 , 16 ].…”

Section: Introductionmentioning

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

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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.

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Deep learning application trial to lung cancer diagnosis for medical sensor systems

Cited by 18 publications

References 1 publication

Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors

Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors

Morphological feature extraction and KNG‐CNN classification of CT images for early lung cancer detection

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

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