Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networks

AbstractIntroductionMalignant tumors of the lung are the most important cause of morbidity and mortality due to cancer all over the world. A rising trend in the incidence of lung cancer has been observed. Histopathological diagnosis of lung cancer is a vital component of patient care. The use of artificial intelligence in the histopathological diagnosis of lung cancer may be a very useful technology in the near future.AimThe aim of the present research project is to determine the effectiveness of convolutional neural networks for the diagnosis of squamous cell carcinoma and adenocarcinoma of the lung by evaluating the digital pathology images of these cancers.Materials & MethodsA total of 15000 digital images of histopathological slides were acquired from the LC2500 dataset. The digital pathology images from lungs are comprised of three classes; class I contains 5000 images of benign lung tissue, class II contains 5,000 images of squamous cell carcinoma of lungs while Class III contains 5,000 images of adenocarcinoma of lungs. Six state of the art off the shelf convolutional neural network architectures, VGG-19, Alex Net, ResNet: ResNet-18, ResNet-34, ResNet-50, and ResNet-101, are used to assess the data, in this comparison study. The dataset was divided into a train set, 55% of the entire data, validation set 20%, and 25% into the test data set.ResultsA number of off the shelf pre-trained (on ImageNet data set) convolutional neural networks are used to classify the histopathological slides into three classes, benign lung tissue, squamous cell carcinoma-lung and adenocarcinoma - lung. The F-1 scores of AlexNet, VGG-19, ResNet-18, ResNet-34, ResNet-50 and ResNet-101, on the test dataset show the result of 0.973, 0.997, 0.986, 0.992, 0.999 and 0.999 respectively.DiscussionThe diagnostic accuracy of more 97% has been achieved for the diagnosis of squamous cell carcinoma and adenocarcinoma of the lungs in the present study. A similar finding has been reported in other studies for the diagnosis of metastasis of breast carcinoma in lymph nodes, basal cell carcinoma, and prostatic cancer.ConclusionThe development of algorithms for the recognition of a specific pattern of the particular malignant tumor by analyzing the digital images will reduce the chance of human errors and improve the efficiency of the laboratory for the rapid and accurate diagnosis of cancer.

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Lung Cancer Prediction Using Ensemble Learning

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit217357 ◽

2021 ◽

pp. 477-482

Author(s):

Vaibhav Narawade ◽

Akash Singh ◽

Mohit Shrivastava ◽

Abhishek Prasad

Keyword(s):

Lung Cancer ◽

Ensemble Learning ◽

Randomized Clinical Trials ◽

Screening Methods ◽

Cure Rate ◽

Data Set ◽

Medical Practitioners ◽

Lung Cancer Patients ◽

Detection Techniques ◽

The Individual

Lung Cancer is the most commonly occurring type of cancer in the world. Despite all the research in the field of lung cancer is still maintains a extremely high mortality rate and a cure rate of of less than 15%. Majority of lung cancer patients are diagnosed at a very advanced stage which is why randomized clinical trials have come under intense scrutiny from the medical practitioners and have led to a new resurgence of interest in its screening methods and development of newer techniques to improve its efficiency. The existing screening and detection techniques have known to be slow, cost ineffective and have other discrepancies such as false positives. Keeping this in mind we propose to use ensemble learning methods to train our data-set to overcome the drawbacks and improve upon the individual algorithms.

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Automatic Detection and Classification of Steel Surface Defect Using Deep Convolutional Neural Networks

Metals ◽

10.3390/met11030388 ◽

2021 ◽

Vol 11 (3) ◽

pp. 388 ◽

Cited By ~ 2

Author(s):

Shuai Wang ◽

Xiaojun Xia ◽

Lanqing Ye ◽

Binbin Yang

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Steel Surface ◽

Surface Defects ◽

Automatic Detection ◽

Deep Convolutional Neural Networks ◽

Data Set ◽

Running Time ◽

Average Running Time

Automatic detection of steel surface defects is very important for product quality control in the steel industry. However, the traditional method cannot be well applied in the production line, because of its low accuracy and slow running speed. The current, popular algorithm (based on deep learning) also has the problem of low accuracy, and there is still a lot of room for improvement. This paper proposes a method combining improved ResNet50 and enhanced faster region convolutional neural networks (faster R-CNN) to reduce the average running time and improve the accuracy. Firstly, the image input into the improved ResNet50 model, which add the deformable revolution network (DCN) and improved cutout to classify the sample with defects and without defects. If the probability of having a defect is less than 0.3, the algorithm directly outputs the sample without defects. Otherwise, the samples are further input into the improved faster R-CNN, which adds spatial pyramid pooling (SPP), enhanced feature pyramid networks (FPN), and matrix NMS. The final output is the location and classification of the defect in the sample or without defect in the sample. By analyzing the data set obtained in the real factory environment, the accuracy of this method can reach 98.2%. At the same time, the average running time is faster than other models.

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Impute, Select, Decision Tree and Naïve Bayes (ISE-DNC): An Ensemble Learning Approach to Classify the Lung Cancer

SSRN Electronic Journal ◽

10.2139/ssrn.3667438 ◽

2020 ◽

Author(s):

Bhanumathi S ◽

Dr. Chandrashekara S N

Keyword(s):

Lung Cancer ◽

Decision Tree ◽

Ensemble Learning ◽

Naive Bayes ◽

Naïve Bayes ◽

Learning Approach

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A hybrid algorithm for lung cancer classification using SVM and Neural Networks

ICT Express ◽

10.1016/j.icte.2020.06.007 ◽

2020 ◽

Author(s):

Pankaj Nanglia ◽

Sumit Kumar ◽

Aparna N. Mahajan ◽

Paramjit Singh ◽

Davinder Rathee

Keyword(s):

Lung Cancer ◽

Neural Networks ◽

Hybrid Algorithm ◽

Cancer Classification

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Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Sensors ◽

10.3390/s20061579 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1579

Author(s):

Dongqi Wang ◽

Qinghua Meng ◽

Dongming Chen ◽

Hupo Zhang ◽

Lisheng Xu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

Channel Model ◽

Expert Knowledge ◽

Automatic Detection ◽

Data Representation ◽

Learning Technology ◽

Arrhythmia Detection ◽

Automatic Feature Extraction

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

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Multi-view Convolutional Recurrent Neural Networks for Lung Cancer Nodule Identification

Neurocomputing ◽

10.1016/j.neucom.2020.06.144 ◽

2021 ◽

Author(s):

Mian Muhammad Naeem Abid ◽

Tehseen Zia ◽

Mubeen Ghafoor ◽

David Windridge

Keyword(s):

Lung Cancer ◽

Neural Networks ◽

Recurrent Neural Networks

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Localization of Scattering Objects Using Neural Networks

Sensors ◽

10.3390/s21010011 ◽

2020 ◽

Vol 21 (1) ◽

pp. 11

Author(s):

Domonkos Haffner ◽

Ferenc Izsák

Keyword(s):

Neural Networks ◽

Plane Waves ◽

Measurement Data ◽

Scattered Wave ◽

Training Data ◽

Data Set ◽

Main Compound ◽

Incident Plane ◽

The Neural Networks ◽

Simulation Package

The localization of multiple scattering objects is performed while using scattered waves. An up-to-date approach: neural networks are used to estimate the corresponding locations. In the scattering phenomenon under investigation, we assume known incident plane waves, fully reflecting balls with known diameters and measurement data of the scattered wave on one fixed segment. The training data are constructed while using the simulation package μ-diff in Matlab. The structure of the neural networks, which are widely used for similar purposes, is further developed. A complex locally connected layer is the main compound of the proposed setup. With this and an appropriate preprocessing of the training data set, the number of parameters can be kept at a relatively low level. As a result, using a relatively large training data set, the unknown locations of the objects can be estimated effectively.

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