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Deep Stacked Sparse Autoencoders – A Breast Cancer Classifier

Muhammad Asif Munir, Muhammad Aqeel Aslam, Muhammad Shafique, Rauf Ahmed, Zafar Mehmood
2021 Mehran University Research Journal of Engineering and Technology  
In this article, Deep Stacked Sparse Autoencoders for breast cancer diagnostic and classification are proposed.  ...  Breast cancer is among one of the non-communicable diseases that is the major cause of women's mortalities around the globe. Early diagnosis of breast cancer has significant death reduction effects.  ...  [9] have proposed deep learning model for breast cancer diagnostic which is ensemble learning based on stacked sparse Autoencoders.  ... 
doi:10.22581/muet1982.2201.05 doaj:303801e6e38e48a9a2e83d8e782daf9c fatcat:jvcvcxyzdnf7jfxc4at4daf2xq

A Survey Of Neural Network-based Cancer Prediction Models From Microarray Data

Maisa Daoud, Michael Mayo
2019 Artificial Intelligence in Medicine  
However, the decision on the number of hidden layers, neurons, hypermeters and learning algorithm is made using trail-and-error techniques.  ...  We identified articles published between 2013-2018 in scientific databases using keywords such as cancer classification, cancer analysis, cancer prediction, cancer clustering and microarray data.  ...  Examples of autoencoder-based approaches are contractive autoencoders, regularized autoencoders, sparse autoencoders and stacked denoising autoencoders which was the most widely used one.  ... 
doi:10.1016/j.artmed.2019.01.006 pmid:30797633 fatcat:yuxczrqxhzcyho3a7h32vcrpi4

Applications of Deep Learning and Reinforcement Learning to Biological Data

Mufti Mahmud, Mohammed Shamim Kaiser, Amir Hussain, Stefano Vassanelli
2018 IEEE Transactions on Neural Networks and Learning Systems  
Overall, recent research in Deep learning (DL), Reinforcement learning (RL), and their combination (Deep RL) promise to revolutionize Artificial Intelligence.  ...  This review article provides a comprehensive survey on the application of DL, RL, and Deep RL techniques in mining Biological data.  ...  Pawel Raif and Dr. Kamal Abu-Hassan for useful discussions during the early stage of the work. This work was supported by the ACSLab (  ... 
doi:10.1109/tnnls.2018.2790388 pmid:29771663 fatcat:6r63zihrfvea7cto4ei3mlvqtu

Applications of Machine Learning Techniques to Predict Diagnostic Breast Cancer

Vikas Chaurasia, Saurabh Pal
2020 SN Computer Science  
This article compares six machine learning (  ...  for selection related predictor and Vectors Machine (SVM) technique for prediction Breast cancer diagnosis 98.606% Kadam et al. [16] 2019 Feature ensemble learning based on Sparse Autoencoders  ...  and Softmax Regression Breast Cancer (prediction benign & malignant) 98.60% Saritas and Yasar [17] 2019 Artificial neural networks and Naïve Bayes classifiers Estimation of having breast  ... 
doi:10.1007/s42979-020-00296-8 fatcat:a55lp36wq5e5vgj4aa2namoy2q

Paper 4..pdf

Arundevsharma@Lkc.Ac.In Arundevsharma@Lkc.Ac.In
2019 Figshare  
A sparse autoencoder with one layer and one with two layers (aka. stacked autoencoder) has been used as the unsupervised feature learning method to learn a sparse representation from unlabeled data which  ...  In order to detect cancer genes & cancer type classification, features are learned. Softmax regression as learning approach for classifier is used.  ... 
doi:10.6084/m9.figshare.9411167 fatcat:6lxol4rilrcy7gourk35qql6ge

Unsupervised Deep Learning Cad Scheme For The Detection Of Malaria In Blood Smear Microscopic Images

Priyadarshini Adyasha Pattanaik, Mohit Mittal, Mohammad Zubair Khan
2020 IEEE Access  
The parameters of the scheme are pre-trained by functional link artificial neural network followed by sparse stacked autoencoder.  ...  The optimum size of the CAD scheme used in this research is 12500-2500-100-50-2, where the input layer has 12500 nodes and Softmax classifier output layer has 2 nodes.  ...  DETECTION METHOD Three-layer Sparse Autoencoder (TAE) is three hidden layers based autoencoder which is also known as deeply stacked autoencoder [54] .  ... 
doi:10.1109/access.2020.2996022 fatcat:bu2pczxsyve3dbhzxuiksif4fq


2021 Zenodo  
Therefore, deep learning is demand due to its ability to automatically discovering the complex relationship between features with significant accuracy and high performance.  ...  Deep neural networks are robust techniques and recently used extensively for building cancer classification models from different types of data.  ...  Lin, and X. Zhao, "A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using RNA-seq data," Comput.  ... 
doi:10.5281/zenodo.6126510 fatcat:vmqa4zuoqrdsxdq7rsflgh362y

Deep Learning assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis

Jing Zheng, Denan Lin, Zhongjun Gao, Shuang Wang, Mingjie He, Jipeng Fan
2020 IEEE Access  
This paper focuses on combining these machine learning approaches with the methods of selecting features and extracting them through evaluating their output using classification and segmentation techniques  ...  Breast cancer is one of the most dangerous diseases and the second largest cause of female cancer death. Breast cancer starts when malignant, cancerous lumps start to grow from the breast cells.  ...  Spare autoencoder (SAE): The use of the cost function regularize leads to a sparse of the autoencoder. This regulator is based on the average neuron output activation value.  ... 
doi:10.1109/access.2020.2993536 fatcat:mfjt7yj3ezd65ipou2os3zpgu4

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

Muhammad Firoz Mridha, Md. Abdul Hamid, Muhammad Mostafa Monowar, Ashfia Jannat Keya, Abu Quwsar Ohi, Md. Rashedul Islam, Jong-Myon Kim
2021 Cancers  
This review focuses on the evolving architectures of deep learning for breast cancer detection.  ...  As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies.  ...  Additionally, special thanks are given to the Advanced Machine Learning lab, BUBT and the Computer Vision & Pattern Recognition Lab, UAP for providing facilities in which to research and publish.  ... 
doi:10.3390/cancers13236116 pmid:34885225 fatcat:ircywikuuvc25laiz3fsrc65bq

Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques and their Analysis

Noreen Fatima, Li Liu, Hong Sha, Haroon Ahmed
2020 IEEE Access  
Many researchers have put their efforts on breast cancer diagnoses and prognoses, every technique has different accuracy rate and it varies for different situations, tools and datasets being used.  ...  This paper presents the comparative analysis of machine learning, deep learning and data mining techniques being used for the prediction of breast cancer.  ...  Sparse Auto Encoder technique was applied on multilayers. Authors used the learning classifier known as Softmax regression to analyze the results [63] .  ... 
doi:10.1109/access.2020.3016715 fatcat:s7vtalybfzga3olr6zkml6n6vi

Recent Advances of Deep Learning in Bioinformatics and Computational Biology

Binhua Tang, Zixiang Pan, Kang Yin, Asif Khateeb
2019 Frontiers in Genetics  
Deep learning, as an emerging branch from machine learning, has exhibited unprecedented performance in quite a few applications from academia and industry.  ...  We highlight the difference and similarity in widely utilized models in deep learning studies, through discussing their basic structures, and reviewing diverse applications and disadvantages.  ...  This work made use of the resources supported by the NSFC-Guangdong Mutual Funds for Super Computing Program (2nd Phase), and the Open Cloud Consortium sponsored project resource, supported in part by  ... 
doi:10.3389/fgene.2019.00214 pmid:30972100 pmcid:PMC6443823 fatcat:ipm6jmfpbrbqdcddy3iyoiyfs4

Cancer Diagnosis Using Deep Learning: A Bibliographic Review

Khushboo Munir, Hassan Elahi, Afsheen Ayub, Fabrizio Frezza, Antonello Rizzi
2019 Cancers  
Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis.  ...  Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer.  ...  Classification of nuclei from breast histopathological images using a stacked sparse autoencoder (SSAE) based algorithm was presented in [24] .  ... 
doi:10.3390/cancers11091235 pmid:31450799 pmcid:PMC6770116 fatcat:ktuuttdu6zc7phj3mahp5yynxq

A new Sparse Auto-encoder based Framework using Grey Wolf Optimizer for Data Classification Problem [article]

Ahmad Mozaffer Karim
2022 arXiv   pre-print
Different training approaches are applied to train sparse autoencoders.  ...  Gray wolf optimization (GWO) is one of the current of those algorithms and is applied to train sparse auto-encoders for this study.  ...  et al. (2019) proposes a fuzzy based logistic regression for gene data feature selection to diagnosis breast cancer disease.  ... 
arXiv:2201.12493v1 fatcat:mcyzarxunbb6ldwtwk6nhhtlke

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

Parita Oza, Paawan Sharma, Samir Patel, Alessandro Bruno
2021 Journal of Imaging  
Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer.  ...  Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate.  ...  An association between lattice-based texture features and breast cancer was evaluated using logistic regression. Li et al.  ... 
doi:10.3390/jimaging7090190 pmid:34564116 pmcid:PMC8466003 fatcat:2r2va44qe5hzhmc6pfysuzphlu

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Jahanzaib Latif, Chuangbai Xiao, Shanshan Tu, Sadaqat Ur Rehman, Azhar Imran, Anas Bilal
2020 IEEE Access  
Most of these methods are based on Machine Learning and Deep Learning Methods, while the auxiliary diagnosis is performed using Rule-based methods.  ...  This review focuses on recently published papers, which are categorized into Rule-Based Methods, Machine Learning (ML) Methods, and Deep Learning (DL) Methods.  ...  Stacked autoencoder and softmax classification methods have been used for the diagnosis and classification of cervical cancer [122] , [123] .  ... 
doi:10.1109/access.2020.3016782 fatcat:j76bwlyrj5dv5mhhsvs4apynje
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