Search Results - Recent Advances in Deep Learning for Biomedical Signal Processing*

Machine learning methods for bio-medical image and signal processing: Recent advances by Wang, Lipo, Sourina, Olga, Erdt, Marius, Wang, Yaoli, Chang, Qing

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Brain Signals Analysis Based Deep Learning Methods: Recent advances in the study of non-invasive brain signals by Essa, Almabrok, Kotte, Hari

“…), Computed Tomography (CT) and others, which will be discussed briefly in this paper. This paper discusses about the currently emerging techniques such as the usage of different Deep Learning (DL…”
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A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers by Zhang, Xiang, Yao, Lina, Wang, Xianzhi, Monaghan, Jessica, McAlpine, David, Zhang, Yu

“… The emerging deep learning techniques have improved the study of brain signals significantly in recent years…”
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Introduction to the Special Issue on Recent Advances on Deep Learning for Medical Signal Analysis by Zhang, Yu-Dong, Dong, Zhengchao, Gorriz, Juan Manuel, Cattani, Carlo, Yang, Ming

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Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals by Tama, Bayu Adhi, Vania, Malinda, Lee, Seungchul, Lim, Sunghoon

“… Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring…”
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Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals by Ay, Betul, Yildirim, Ozal, Talo, Muhammed, Baloglu, Ulas Baran, Aydin, Galip, Puthankattil, Subha D., Acharya, U. Rajendra

“… (LSTM) architectures to detect depression using EEG signals. In the deep model, temporal properties of the signals are learned with CNN layers and the sequence learning process is provided through the LSTM layers…”
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Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning by Amin, Javaria, Sharif, Muhammad, Gul, Nadia, Raza, Mudassar, Anjum, Muhammad Almas, Nisar, Muhammad Wasif, Bukhari, Syed Ahmad Chan

“… In this manuscript, a deep learning model is deployed to predict input slices as a tumor (unhealthy)/non-tumor (healthy). This manuscript employs a high pass filter image…”
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Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques by Acharya, U. Rajendra, Fernandes, Steven Lawrence, WeiKoh, Joel En, Ciaccio, Edward J., Fabell, Mohd Kamil Mohd, Tanik, U. John, Rajinikanth, V., Yeong, Chai Hong

“…The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer’s disease. The…”
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A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning by Amin, Javeria, Sharif, Muhammad, Yasmin, Mussarat, Saba, Tanzila, Anjum, Muhammad Almas, Fernandes, Steven Lawrence

“…Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain…”
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Classification of Mammogram Images Using Multiscale all Convolutional Neural Network (MA-CNN) by Agnes, S. Akila, Anitha, J., Pandian, S. Immanuel Alex, Peter, J. Dinesh

“… Convolutional neural networks are excellent in extracting the task specific features, since the feature learning is associated with classification task in order to attain the improved performance…”
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Stomach Deformities Recognition Using Rank-Based Deep Features Selection by Khan, Muhammad Attique, Sharif, Muhammad, Akram, Tallha, Yasmin, Mussarat, Nayak, Ramesh Sunder

“… Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion…”
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Lung Nodule Detection based on Ensemble of Hand Crafted and Deep Features by Saba, Tanzila, Sameh, Ahmed, Khan, Fatima, Shad, Shafqat Ali, Sharif, Muhammad

“…) lung nodule segmentation using Otsu threshold followed by morphological operation; (2) extraction of geometrical, texture and deep learning features for selecting optimal features…”
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Fog Computing Employed Computer Aided Cancer Classification System Using Deep Neural Network in Internet of Things Based Healthcare System by Rajan, J. Pandia, Rajan, S. Edward, Martis, Roshan Joy, Panigrahi, B. K.

“… In this research work, we proposed a novel method which utilizes a modified vesselness measurement and a Deep Convolutional Neural Network (DCNN…”
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A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images by Raghavendra, U., Gudigar, Anjan, Bhandary, Sulatha V., Rao, Tejaswi N., Ciaccio, Edward J., Acharya, U. Rajendra

“… Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach…”
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Recent advances in medical image processing for the evaluation of chronic kidney disease by Alnazer, Israa, Bourdon, Pascal, Urruty, Thierry, Falou, Omar, Khalil, Mohamad, Shahin, Ahmad, Fernandez-Maloigne, Christine

“…•The role of deep learning in automatic renal segmentation from medical images is summarized…”
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A concise review on food quality assessment using digital image processing by Meenu, Maninder, Kurade, Chinmay, Neelapu, Bala Chakravarthy, Kalra, Sahil, Ramaswamy, Hosahalli S., Yu, Yong

“…Recent advances in signal processing technology and computational power have increased the attention towards computer vision-based techniques in diverse applications such as agriculture, food…”
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Signal to Image Conversion and Convolutional Neural Networks for Physiological Signal Processing: A Review by Vidyasagar, K.E.Ch, Revanth Kumar, K, Anantha Sai, G.N.K., Ruchita, Munagala, Saikia, Manob Jyoti

“… Recent studies have demonstrated the effectiveness of deep convolutional neural networks (CNNs…”
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IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge by Porwal, Prasanna, Pachade, Samiksha, Kokare, Manesh, Deshmukh, Girish, Son, Jaemin, Bae, Woong, Liu, Lihong, Wang, Jianzong, Liu, Xinhui, Gao, Liangxin, Wu, TianBo, Xiao, Jing, Wang, Fengyan, Yin, Baocai, Wang, Yunzhi, Danala, Gopichandh, He, Linsheng, Choi, Yoon Ho, Lee, Yeong Chan, Jung, Sang-Hyuk, Li, Zhongyu, Sui, Xiaodan, Wu, Junyan, Li, Xiaolong, Zhou, Ting, Toth, Janos, Baran, Agnes, Kori, Avinash, Chennamsetty, Sai Saketh, Safwan, Mohammed, Alex, Varghese, Lyu, Xingzheng, Cheng, Li, Chu, Qinhao, Li, Pengcheng, Ji, Xin, Zhang, Sanyuan, Shen, Yaxin, Dai, Ling, Saha, Oindrila, Sathish, Rachana, Melo, Tânia, Araújo, Teresa, Harangi, Balazs, Sheng, Bin, Fang, Ruogu, Sheet, Debdoot, Hajdu, Andras, Zheng, Yuanjie, Mendonça, Ana Maria, Zhang, Shaoting, Campilho, Aurélio, Zheng, Bin, Shen, Dinggang, Giancardo, Luca, Quellec, Gwenolé, Mériaudeau, Fabrice

“…•Presents various deep learning and handcrafted features based participating approaches.•Discusses the lessons learnt from analysis of the methods submitted to this challenge. [Display omitted…”
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Medical Image Analysis using Convolutional Neural Networks: A Review by Anwar, Syed Muhammad, Majid, Muhammad, Qayyum, Adnan, Awais, Muhammad, Alnowami, Majdi, Khan, Muhammad Khurram

“… The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area…”
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FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising by Zhou, Bo, Xie, Huidong, Liu, Qiong, Chen, Xiongchao, Guo, Xueqi, Feng, Zhicheng, Hou, Jun, Zhou, S. Kevin, Li, Biao, Rominger, Axel, Shi, Kuangyu, Duncan, James S., Liu, Chi

“…), thus affecting diagnosis and other downstream tasks. Recent advances in deep learning have shown great potential in improving low-count PET image quality…”
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