The state of the art of deep learning models in medical science and their challenges

2020 ◽  
Author(s):  
Chandradeep Bhatt ◽  
Indrajeet Kumar ◽  
V. Vijayakumar ◽  
Kamred Udham Singh ◽  
Abhishek Kumar
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Medical Science ◽  
The State ◽  
Learning Models

scholarly journals An Improved CNN Model for Within-Project Software Defect Prediction

2019 ◽  
Vol 9 (10) ◽  
pp. 2138 ◽  
Author(s):  
Cong Pan ◽  
Minyan Lu ◽  
Biao Xu ◽  
Houleng Gao
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Source Code ◽  
The State ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Learning Models ◽  
Software Defect ◽  
Original Dataset ◽  
Holdout Validation

To improve software reliability, software defect prediction is used to find software bugs and prioritize testing efforts. Recently, some researchers introduced deep learning models, such as the deep belief network (DBN) and the state-of-the-art convolutional neural network (CNN), and used automatically generated features extracted from abstract syntax trees (ASTs) and deep learning models to improve defect prediction performance. However, the research on the CNN model failed to reveal clear conclusions due to its limited dataset size, insufficiently repeated experiments, and outdated baseline selection. To solve these problems, we built the PROMISE Source Code (PSC) dataset to enlarge the original dataset in the CNN research, which we named the Simplified PROMISE Source Code (SPSC) dataset. Then, we proposed an improved CNN model for within-project defect prediction (WPDP) and compared our results to existing CNN results and an empirical study. Our experiment was based on a 30-repetition holdout validation and a 10 * 10 cross-validation. Experimental results showed that our improved CNN model was comparable to the existing CNN model, and it outperformed the state-of-the-art machine learning models significantly for WPDP. Furthermore, we defined hyperparameter instability and examined the threat and opportunity it presents for deep learning models on defect prediction.

scholarly journals A Novel Bio-Inspired Deep Learning Approach for Liver Cancer Diagnosis

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 80 ◽  
Author(s):  
Rania M. Ghoniem
Keyword(s):  
Deep Learning ◽  
Liver Cancer ◽  
State Of The Art ◽  
The State ◽  
Convergence Time ◽  
Computational Time ◽  
Learning Approach ◽  
Liver Lesions ◽  
Learning Models ◽  
Abc Algorithm

Current research on computer-aided diagnosis (CAD) of liver cancer is based on traditional feature engineering methods, which have several drawbacks including redundant features and high computational cost. Recent deep learning models overcome these problems by implicitly capturing intricate structures from large-scale medical image data. However, they are still affected by network hyperparameters and topology. Hence, the state of the art in this area can be further optimized by integrating bio-inspired concepts into deep learning models. This work proposes a novel bio-inspired deep learning approach for optimizing predictive results of liver cancer. This approach contributes to the literature in two ways. Firstly, a novel hybrid segmentation algorithm is proposed to extract liver lesions from computed tomography (CT) images using SegNet network, UNet network, and artificial bee colony optimization (ABC), namely, SegNet-UNet-ABC. This algorithm uses the SegNet for separating liver from the abdominal CT scan, then the UNet is used to extract lesions from the liver. In parallel, the ABC algorithm is hybridized with each network to tune its hyperparameters, as they highly affect the segmentation performance. Secondly, a hybrid algorithm of the LeNet-5 model and ABC algorithm, namely, LeNet-5/ABC, is proposed as feature extractor and classifier of liver lesions. The LeNet-5/ABC algorithm uses the ABC to select the optimal topology for constructing the LeNet-5 network, as network structure affects learning time and classification accuracy. For assessing performance of the two proposed algorithms, comparisons have been made to the state-of-the-art algorithms on liver lesion segmentation and classification. The results reveal that the SegNet-UNet-ABC is superior to other compared algorithms regarding Jaccard index, Dice index, correlation coefficient, and convergence time. Moreover, the LeNet-5/ABC algorithm outperforms other algorithms regarding specificity, F1-score, accuracy, and computational time.

Retinal Healthcare Diagnosis Approaches with Deep Learning Techniques

2021 ◽  
Vol 11 (3) ◽  
pp. 846-855
Author(s):  
Hamza Riaz ◽  
Jisu Park ◽  
Peter H. Kim ◽  
Jungsuk Kim
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
State Of The Art ◽  
Medical Science ◽  
Retinal Diseases ◽  
Learning Models ◽  
Convolutional Networks ◽  
Learning Techniques ◽  
Diagnosis Accuracy ◽  
A Minor

The retina is an important organ of the human body, with a crucial function in the vision mechanism. A minor disturbance in the retina can cause various abnormalities in the eye, as well as complex retinal diseases such as diabetic retinopathy. To diagnose such diseases in early stages, many researchers are incorporating machine learning (ML) technique. The combination of medical science with ML improves the healthcare diagnosis systems of hospitals, clinics, and other providers. Recently, AI-based healthcare diagnosis systems assist clinicians in handling more patients in less time and improves diagnosis accuracy. In this paper, we review cutting-edge AI-based retinal diagnosis technologies. This article also briefly describes the potential of the latest densely connected convolutional networks (DenseNets) to improve the performance of diagnosis systems. Moreover, this paper focuses on state-of-the-art results from comprehensive investigations in retinal diagnosis and the development of AI-based retinal healthcare diagnosis approaches with deep-learning models.

scholarly journals Analysis of the impact of parameters in TextGCN

2021 ◽  
Author(s):  
Henrique Varella Ehrenfried ◽  
Eduardo Federal University of Paraná Curitiba, Paraná, Brazil
Keyword(s):  
Deep Learning ◽  
Side Effect ◽  
State Of The Art ◽  
The State ◽  
Learning Rate ◽  
Learning Models ◽  
Early Stopping ◽  
The Impact

Deep learning models uses many parameters to work properly. Asthey become more complex, the authors of these novel models cannotexplore in their papers the variation of each parameter of theirmodel. Therefore, this work describes an analysis of the impact offour different parameters (Early Stopping, Learning Rate, Dropout,and Hidden 1) in the TextGCN Model. This evaluation used fourdatasets considered in the original TextGCN publication, obtainingas a side-effect small improvements in the results of three of them.The most relevant conclusion is that these parameters influence theconvergence and accuracy, although they individually do not constitutestrong support when aiming to improve the model’s resultsreported as the state-of-the-art.

scholarly journals Temporal Feature Aggregation for Text Classification Based on Ensembled Deep-Learning Models

2021 ◽  
pp. 23-28
Author(s):  
Jiali Yu ◽  
◽  
Zhiliang Qin ◽  
Linghao Lin ◽  
Yu Qin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Cross Validation ◽  
State Of The Art ◽  
The State ◽  
Learning Models ◽  
Feature Aggregation ◽  
Validation Score ◽  
The Cross ◽  
Temporal Feature

In this paper, we focus on the text classification task, which is a most import task in the area of Natural Language Processing (NLP). We propose an innovative convolutional neural network (CNN) model to perform temporal feature aggregation (TFA) effectively, which has a highly representative capacity to extract sequential features from vectorized numerical embeddings. First, we feed embedded vectors into a bi-directional LSTM (Bi-LSTM) model to capture the contextual information of each word. Afterwards, we propose to use the state-of-the-art deep-learning models as key components of the architecture, i.e., the Xception model and the WaveNet model, to extract temporal features from deep convolutional layers concurrently. To facilitate an effective feature fusion, we concatenate the outputs of two component models before forwarding to a drop-out layer to alleviate over-fitting and subsequently a fully-connected dense layer to perform the final classification of input texts. Experiments demonstrate that the proposed method achieves performance comparable to the state-of-the-art models while at a significantly lower computational complexity. Our approach obtains the cross-validation score of 95.83% for the Quora Insincere Question Classification (QIQC) dataset, and the cross-validation score of 83.10% for the Spooky Author Identification (SAI) dataset, respectively, which are among the best published results. The proposed method can be readily generalized to signal processing tasks, e.g., environmental sound classification (ESC) and machine fault analysis (MFA).

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

Named Entity Recognition and Relation Extraction

2021 ◽  
Vol 54 (1) ◽  
pp. 1-39
Author(s):  
Zara Nasar ◽  
Syed Waqar Jaffry ◽  
Muhammad Kamran Malik
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
The State ◽  
Entity Recognition ◽  
Joint Models ◽  
Named Entity ◽  
Textual Data ◽  
Benchmark Datasets

With the advent of Web 2.0, there exist many online platforms that result in massive textual-data production. With ever-increasing textual data at hand, it is of immense importance to extract information nuggets from this data. One approach towards effective harnessing of this unstructured textual data could be its transformation into structured text. Hence, this study aims to present an overview of approaches that can be applied to extract key insights from textual data in a structured way. For this, Named Entity Recognition and Relation Extraction are being majorly addressed in this review study. The former deals with identification of named entities, and the latter deals with problem of extracting relation between set of entities. This study covers early approaches as well as the developments made up till now using machine learning models. Survey findings conclude that deep-learning-based hybrid and joint models are currently governing the state-of-the-art. It is also observed that annotated benchmark datasets for various textual-data generators such as Twitter and other social forums are not available. This scarcity of dataset has resulted into relatively less progress in these domains. Additionally, the majority of the state-of-the-art techniques are offline and computationally expensive. Last, with increasing focus on deep-learning frameworks, there is need to understand and explain the under-going processes in deep architectures.

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