Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection

Zhou, Qing; Yong, Binbin; Lv, Qingquan; Shen, Jun; Wang, Xin

doi:10.1109/access.2020.2977680

Cited by 19 publications

(10 citation statements)

References 22 publications

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“…Among all the positive examples in the data, recall means the positive cases, which the classifier correctly predicted [64]. Sensitivity is a term that is occasionally used to describe it [65].…”

Section: Resultsmentioning

confidence: 99%

A Supervised Learning Identification System for Prognosis of Breast Cancer

Rawat

Gulati

Kaur

et al. 2022

Mathematical Problems in Engineering

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Breast cancer is one of the most dangerous cancers, accounting for a large number of fatalities each year. It is the leading cause of mortality among women globally. It is getting a lot of interest in the scientific community because of its possible life-threatening danger. As a consequence, many machine learning methods (MLMs) have been modified to provide the best results for early diagnosis of this malignancy. Machine learning methods (MLMs) offer several beneficial implications in breast cancer, including early prognosis, detection, and diagnosis. Compared to traditional statistical analysis, machine learning methods (MLMs) have the capacity to improve the analysis of various health data, such as unstructured, complicated, and noisy data. With the demanding prevalence of breast cancer and the arrival of “data reformation,” it is thus imperative to mention the ethical consequences of machine learning (ML) on society and cancer care. It offers conclusively strong tools, smart methods, and efficient algorithms that can help in the prognosis of breast cancer. The focus of this review is on supervised techniques such as classification and regression that may be implemented and used for breast cancer data analysis. Some supervised learning methods like Naive Bayes, AdaBoost, and support vector machine are presented in this work in the early identification of breast cancer. These algorithms have been analyzed for their accuracy and efficiency using various assessment metrics and methods.

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Section: Resultsmentioning

confidence: 99%

A Supervised Learning Identification System for Prognosis of Breast Cancer

Rawat

Gulati

Kaur

et al. 2022

Mathematical Problems in Engineering

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show abstract

“…The COVID-19 detection analysis was conducted based on 10 numbers of population and 25 maximum numbers iterations. The proposed meta-heuristic algorithm was examined with additional algorithms like "Particle Swarm Optimization (PSO) [20], Grey Wolf Optimizer (GWO) [9], Whale Optimization Algorithm (WOA) [35], TSA [32], classifiers such as Support Vector Machine (SVM) [11], Auto encoder [43], Naive Bayes [5], Ensemble learning [24], RNN [42], LSTM [23] and SA-TSA".…”

Section: Methodsmentioning

confidence: 99%

“…The kernel function κ is used to convert the input data into the required form which is optimized using the SA-TSA algorithm. (b) Autoencoder [ 43 ]: The proposed COVID-19 model uses an autoencoder to get the distortion-less feature classes by mapping the features, where the hidden neurons are optimized using SA-TSA. The optimization of hidden neurons has been done by adjusting the values among 10 to 50.The mapping is done by transferring the data into encoder, which converts the high-dimensional data into low-dimensional data and finally getting back the high-dimensional data using decoder.…”

Section: Ensemble Learning With Cnn-based Deep Features For Covid-19 Detectionmentioning

confidence: 99%

Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis

Das¹

2021

Multimed Tools Appl

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COVID-19 disease is a major health calamity in twentieth century, in which the infection is spreading at the global level. Developing countries like Bangladesh, India, and others are still facing a delay in recognizing COVID-19 cases. Hence, there is a need for immediate recognition with perfect identification of infection. This clear visualization helps to save the life of suspected COVID-19 patients. With the help of traditional RT-PCR testing, the combination of medical images and deep learning classifiers delivers more hopeful results with high accuracy in the prediction and recognition of COVID-19 cases. COVID-19 disease is recently researched through sample chest X-ray images, which have already proven its efficiency in lung diseases. To emphasize corona virus testing methods and to control the community spreading, the automatic detection process of COVID-19 is processed through the detailed medication reports from medical images. Although there are numerous challenges in the manual understanding of traces in COVID-19 infection from X-ray, the subtle differences among normal and infected X-rays can be traced by the data patterns of Convolutional Neural Network (CNN). To improve the detection performance of CNN, this paper plans to develop an Ensemble Learning with CNN-based Deep Features (EL-CNN-DF). In the initial phase, image scaling and median filtering perform the pre-processing of the chest X-ray images gathered from the benchmark source. The second phase is lung segmentation, which is the significant step for COVID detection. It is accomplished by the Adaptive Activation Function-based U-Net (AAF-U-Net). Once the lungs are segmented, it is subjected to novel EL-CNN-DF, in which the deep features are extracted from the pooling layer of CNN, and the fully connected layer of CNN are replaced with the three classifiers termed “Support Vector Machine (SVM), Autoencoder, Naive Bayes (NB)”. The final detection of COVID-19 is done by these classifiers, in which high ranking strategy is utilized. As a modification, a Self Adaptive-Tunicate Swarm Algorithm (SA-TSA) is adopted as a boosting algorithm to enhance the performance of segmentation and detection. The overall analysis has shown that the precision of the enhanced CNN by using SA-TSA was 1.02%, 4.63%, 3.38%, 1.62%, 1.51% and 1.04% better than SVM, autoencoder, NB, Ensemble, RNN and LSTM respectively. The comparative performance analysis on existing model proves that the proposed algorithm is better than other algorithms in terms of segmentation and classification of COVID-19 detection.

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“…The concept of auto-encoder was proposed earlier, and was originally applied to high-dimensional complex data processing, which promoted the development of neural networks [42][43][44] . The self-encoder is an unsupervised learning algorithm in the deep learning algorithm, to be more precise, a self-supervised learning algorithm whose label data is derived from the input samples.…”

Section: Automatic Encodermentioning

confidence: 99%

Image Target Recognition Model of Multi- Channel Structure Convolutional Neural Network Training Automatic Encoder

et al. 2020

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The self-encoder is a typical unsupervised deep learning algorithm. In the field of unsupervised learning, it is very popular with researchers. Therefore, in view of the shortage of labeled training samples, the convolution kernel of a typical convolutional neural network is set by experience, and the network structure is fixed and it is difficult to re-learn later. This paper combines the convolutional neural network and the automatic encoder, and proposes a multi-based the method of integrated network structure to extract the features of the image for recognition. First, the SAE pre-trained CNN model convolution kernel is used to pre-train based on the classic CNN structure. Secondly, input and process image data of different scales to extract image space and spectral features respectively. Then, construct multiple channels, and use different scale filters and sampling intervals for different channels. Finally, after one layer of down sampling, the feature maps obtained from multiple channels are input into the fully connected layer, and after a hidden layer, the features finally used for classification are obtained. Experimental results show that the proposed method uses sparse automatic coding for pre-training time efficiency increased by 50%, and can further improve the recognition accuracy, the highest recognition rate reached 0.985. INDEX TERMS Convolutional neural network; multi-channel; training; automatic encoder; recognition.

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Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection

Cited by 19 publications

References 22 publications

A Supervised Learning Identification System for Prognosis of Breast Cancer

A Supervised Learning Identification System for Prognosis of Breast Cancer

Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis

Image Target Recognition Model of Multi- Channel Structure Convolutional Neural Network Training Automatic Encoder

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