scholarly journals CNN-based Diagnosis System on Skin Cancer using Ensemble Method Weighted by Cubic Precision

2021 ◽  
Author(s):  
Pengcheng Jiang
Keyword(s):  
User Interface ◽  
Skin Cancer ◽  
Language Processing ◽  
Classification Accuracy ◽  
Model Updating ◽  
Early Stage ◽  
Model Performance ◽  
Diagnosis System ◽  
Cancer Symptoms ◽  
User Friendly

<i>Abstract</i>— One of the most prevalent diseases, skin cancer, has been proven to be treatable at an early stage. Thus, techniques that allow individuals to identify skin cancer symptoms early are in great demand. This paper proposed an interactive skin lesion diagnosis system based on the ensemble of multiple sophisticated CNN models for image classification. The performance of ResNet50, ResNeXt50, ResNeXt101, EfficientNetB4, Mobile-NetV2, MobileNetV3, and MnasNet are investigated separately as ensemble components. Then, using various criteria, we constructed ensembles and compared the accuracy they achieved. Moreover, we designed a method to update the ensemble for new data and examined its performance. In addition, a few natural language processing (NLP) techniques were used to make our system more user-friendly. To integrate all the functionalities, we built a user interface with PyQt5. As a result, MobileNetV3 achieved 91.02% as the best accuracy among all single models; ensemble weighted by cubic precision achieved 92.84% accuracy as the highest one in this study; a notable improvement in accuracy demonstrated the effectiveness of the model updating approach, and a system with all of the desired features was successfully developed. These findings benefit in two aspects. For model performance, applying cubic precisions can increase ensemble learning classification accuracy. For the developed diagnosis system, it can aid in the

scholarly journals CNN-based Diagnosis System on Skin Cancer using Ensemble Method Weighted by Cubic Precision

2021 ◽  
Author(s):  
Pengcheng Jiang
Keyword(s):  
User Interface ◽  
Skin Cancer ◽  
Language Processing ◽  
Classification Accuracy ◽  
Model Updating ◽  
Early Stage ◽  
Model Performance ◽  
Diagnosis System ◽  
Cancer Symptoms ◽  
User Friendly

<i>Abstract</i>— One of the most prevalent diseases, skin cancer, has been proven to be treatable at an early stage. Thus, techniques that allow individuals to identify skin cancer symptoms early are in great demand. This paper proposed an interactive skin lesion diagnosis system based on the ensemble of multiple sophisticated CNN models for image classification. The performance of ResNet50, ResNeXt50, ResNeXt101, EfficientNetB4, Mobile-NetV2, MobileNetV3, and MnasNet are investigated separately as ensemble components. Then, using various criteria, we constructed ensembles and compared the accuracy they achieved. Moreover, we designed a method to update the ensemble for new data and examined its performance. In addition, a few natural language processing (NLP) techniques were used to make our system more user-friendly. To integrate all the functionalities, we built a user interface with PyQt5. As a result, MobileNetV3 achieved 91.02% as the best accuracy among all single models; ensemble weighted by cubic precision achieved 92.84% accuracy as the highest one in this study; a notable improvement in accuracy demonstrated the effectiveness of the model updating approach, and a system with all of the desired features was successfully developed. These findings benefit in two aspects. For model performance, applying cubic precisions can increase ensemble learning classification accuracy. For the developed diagnosis system, it can aid in the

scholarly journals Graphical User Interface (GUI) of Digital Index Evaluation System for Finger Clubbing Identification

2017 ◽  
Vol 14 (2) ◽  
pp. 99 ◽  
Author(s):  
Sharifah Masniah Wan Masra ◽  
K. L. Goh ◽  
Mohd Saufee Muhammad ◽  
Rahardjo Darmanto Djojodibroto ◽  
Rohana Sapawi ◽  
...  
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Evaluation System ◽  
Early Stage ◽  
Index Evaluation ◽  
Busy Clinic ◽  
Index Evaluation System ◽  
Clinic Practice ◽  
User Friendly ◽  
Short Time

This paper presents the development of a Graphical User Interface (GUI) for calculating the sum of nail-fold (NF) and distal interphalangeal joint (DIP) ratios for all ten fingers. The sum of NF:DIP ratios for all ten fingers leads to the Digital Index (DI) that was used as the measure for identifying and determining the presence of finger clubbing symptom. This GUI system was developed to serve as a simple and user-friendly interface for clinicians to calculate DI value of patients in a busy clinic practice. It is also equipped with the capability to keep the patient’s past diagnosis medical check-up data for future monitoring purposes. The result shows that the developed system helps the clinicians to perform calculation of DI value and identify the presence of finger clubbing in a very short time. The average time taken to measure both NF and DIP circumferences using Finger Clubbing Meter, and to compute DI values using Digital Index Evaluation System (DIES) interface is 6:36 ± 1:24 minutes (Mean ± SD) .This system is expected to contribute in detecting the finger clubbing problem at early stage of so the treatment can be performed immediately.

scholarly journals Graphical User Interface (GUI) of Digital Index Evaluation System for Finger Clubbing Identification

2017 ◽  
Vol 14 (2) ◽  
pp. 99 ◽  
Author(s):  
S. M. W. Masra ◽  
K. L. Goh ◽  
M. S. Muhammad ◽  
R. D. Djojodibroto ◽  
R. Sapawi ◽  
...  
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Evaluation System ◽  
Early Stage ◽  
Index Evaluation ◽  
Busy Clinic ◽  
Index Evaluation System ◽  
Clinic Practice ◽  
User Friendly ◽  
Short Time

This paper presents the development of a Graphical User Interface (GUI) for calculating the sum of nail-fold (NF) and distal interphalangeal joint (DIP) ratios for all ten fingers. The sum of NF:DIP ratios for all ten fingers leads to the Digital Index (DI) that was used as the measure for identifying and determining the presence of finger clubbing symptom. This GUI system was developed to serve as a simple and user-friendly interface for clinicians to calculate DI value of patients in a busy clinic practice. It is also equipped with the capability to keep the patient’s past diagnosis medical check-up data for future monitoring purposes. The result shows that the developed system helps the clinicians to perform calculation of DI value and identify the presence of finger clubbing in a very short time. The average time taken to measure both NF and DIP circumferences using Finger Clubbing Meter, and to compute DI values using Digital Index Evaluation System (DIES) interface is 6:36 ± 1:24 minutes (Mean ± SD) .This system is expected to contribute in detecting the finger clubbing problem at early stage of so the treatment can be performed immediately.

scholarly journals Prediction of ambulatory outcome in patients with corona radiata infarction using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jeoung Kun Kim ◽  
Yoo Jin Choo ◽  
Hyunkwang Shin ◽  
Gyu Sang Choi ◽  
Min Cheol Chang
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Early Stage ◽  
Model Performance ◽  
Area Under The Curve ◽  
Brain Magnetic Resonance Imaging ◽  
Validation Dataset ◽  
Corona Radiata ◽  
Magnetic Resonance Imaging Mri ◽  
Validation Set

AbstractDeep learning (DL) is an advanced machine learning approach used in diverse areas such as bioinformatics, image analysis, and natural language processing. Here, using brain magnetic resonance imaging (MRI) data obtained at early stages of infarcts, we attempted to develop a convolutional neural network (CNN) to predict the ambulatory outcome of corona radiata infarction at six months after onset. We retrospectively recruited 221 patients with corona radiata infarcts. A favorable outcome of ambulatory function was defined as a functional ambulation category (FAC) score of ≥ 4 (able to walk without a guardian’s assistance), and a poor outcome of ambulatory function was defined as an FAC score of < 4. We used a CNN algorithm. Of the included subjects, 69.7% (n = 154) were assigned randomly to the training set and the remaining 30.3% (n = 67) were assigned to the validation set to measure the model performance. The area under the curve was 0.751 (95% CI 0.649–0.852) for the prediction of ambulatory function with the validation dataset using the CNN model. We demonstrated that a CNN model trained using brain MRIs captured at an early stage after corona radiata infarction could be helpful in predicting long-term ambulatory outcomes.

An Integrated Feature Selection Algorithm for Cancer Classification using Gene Expression Data

2019 ◽  
Vol 21 (9) ◽  
pp. 631-645 ◽  
Author(s):  
Saeed Ahmed ◽  
Muhammad Kabir ◽  
Zakir Ali ◽  
Muhammad Arif ◽  
Farman Ali ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Classification Accuracy ◽  
Early Stage ◽  
Small Sample Size ◽  
Feature Selection Method ◽  
Small Sample ◽  
Expression Data ◽  
Base Function

Aim and Objective: Cancer is a dangerous disease worldwide, caused by somatic mutations in the genome. Diagnosis of this deadly disease at an early stage is exceptionally new clinical application of microarray data. In DNA microarray technology, gene expression data have a high dimension with small sample size. Therefore, the development of efficient and robust feature selection methods is indispensable that identify a small set of genes to achieve better classification performance. Materials and Methods: In this study, we developed a hybrid feature selection method that integrates correlation-based feature selection (CFS) and Multi-Objective Evolutionary Algorithm (MOEA) approaches which select the highly informative genes. The hybrid model with Redial base function neural network (RBFNN) classifier has been evaluated on 11 benchmark gene expression datasets by employing a 10-fold cross-validation test. Results: The experimental results are compared with seven conventional-based feature selection and other methods in the literature, which shows that our approach owned the obvious merits in the aspect of classification accuracy ratio and some genes selected by extensive comparing with other methods. Conclusion: Our proposed CFS-MOEA algorithm attained up to 100% classification accuracy for six out of eleven datasets with a minimal sized predictive gene subset.

scholarly journals BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Olga Majewska ◽  
Charlotte Collins ◽  
Simon Baker ◽  
Jari Björne ◽  
Susan Windisch Brown ◽  
...  
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Model Performance ◽  
Representation Learning ◽  
Verb Classes ◽  
Expert Annotation ◽  
Biomedical Texts ◽  
Time Required ◽  
Verb Meaning

Abstract Background Recent advances in representation learning have enabled large strides in natural language understanding; However, verbal reasoning remains a challenge for state-of-the-art systems. External sources of structured, expert-curated verb-related knowledge have been shown to boost model performance in different Natural Language Processing (NLP) tasks where accurate handling of verb meaning and behaviour is critical. The costliness and time required for manual lexicon construction has been a major obstacle to porting the benefits of such resources to NLP in specialised domains, such as biomedicine. To address this issue, we combine a neural classification method with expert annotation to create BioVerbNet. This new resource comprises 693 verbs assigned to 22 top-level and 117 fine-grained semantic-syntactic verb classes. We make this resource available complete with semantic roles and VerbNet-style syntactic frames. Results We demonstrate the utility of the new resource in boosting model performance in document- and sentence-level classification in biomedicine. We apply an established retrofitting method to harness the verb class membership knowledge from BioVerbNet and transform a pretrained word embedding space by pulling together verbs belonging to the same semantic-syntactic class. The BioVerbNet knowledge-aware embeddings surpass the non-specialised baseline by a significant margin on both tasks. Conclusion This work introduces the first large, annotated semantic-syntactic classification of biomedical verbs, providing a detailed account of the annotation process, the key differences in verb behaviour between the general and biomedical domain, and the design choices made to accurately capture the meaning and properties of verbs used in biomedical texts. The demonstrated benefits of leveraging BioVerbNet in text classification suggest the resource could help systems better tackle challenging NLP tasks in biomedicine.

scholarly journals An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Derya Avci ◽  
Akif Dogantekin
Keyword(s):  
Genetic Algorithm ◽  
Parkinson Disease ◽  
Extreme Learning Machine ◽  
Classification Accuracy ◽  
Disease Diagnosis ◽  
Major Public Health Problem ◽  
Diagnosis System ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Neurons

Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtained from the UCI machine learning database. In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using a genetic algorithm (GA). The performance of the proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specificity analysis, and ROC curves. The calculated highest classification accuracy of the proposed GA-WK-ELM method is found as 96.81%.

scholarly journals Medical Text Classification Using Hybrid Deep Learning Models with Multihead Attention

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Sunil Kumar Prabhakar ◽  
Dong-Ok Won
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Text Classification ◽  
Patient Information ◽  
Classification Accuracy ◽  
Learning Model ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Medical Text ◽  
Deep Learning Model

To unlock information present in clinical description, automatic medical text classification is highly useful in the arena of natural language processing (NLP). For medical text classification tasks, machine learning techniques seem to be quite effective; however, it requires extensive effort from human side, so that the labeled training data can be created. For clinical and translational research, a huge quantity of detailed patient information, such as disease status, lab tests, medication history, side effects, and treatment outcomes, has been collected in an electronic format, and it serves as a valuable data source for further analysis. Therefore, a huge quantity of detailed patient information is present in the medical text, and it is quite a huge challenge to process it efficiently. In this work, a medical text classification paradigm, using two novel deep learning architectures, is proposed to mitigate the human efforts. The first approach is that a quad channel hybrid long short-term memory (QC-LSTM) deep learning model is implemented utilizing four channels, and the second approach is that a hybrid bidirectional gated recurrent unit (BiGRU) deep learning model with multihead attention is developed and implemented successfully. The proposed methodology is validated on two medical text datasets, and a comprehensive analysis is conducted. The best results in terms of classification accuracy of 96.72% is obtained with the proposed QC-LSTM deep learning model, and a classification accuracy of 95.76% is obtained with the proposed hybrid BiGRU deep learning model.

scholarly journals Word and sentence embedding tools to measure semantic similarity of Gene Ontology terms by their definitions

2017 ◽  
Author(s):  
Dat Duong ◽  
Wasi Uddin Ahmad ◽  
Eleazar Eskin ◽  
Kai-Wei Chang ◽  
Jingyi Jessica Li
Keyword(s):  
Neural Network ◽  
Gene Ontology ◽  
Language Processing ◽  
Classification Accuracy ◽  
Dimensional Space ◽  
Similarity Score ◽  
Biological Functions ◽  
Word Similarity ◽  
True Protein ◽  
Go Terms

AbstractThe Gene Ontology (GO) database contains GO terms that describe biological functions of genes. Previous methods for comparing GO terms have relied on the fact that GO terms are organized into a tree structure. In this paradigm, the locations of two GO terms in the tree dictate their similarity score. In this paper, we introduce two new solutions for this problem, by focusing instead on the definitions of the GO terms. We apply neural network based techniques from the natural language processing (NLP) domain. The first method does not rely on the GO tree, whereas the second indirectly depends on the GO tree. In our first approach, we compare two GO definitions by treating them as two unordered sets of words. The word similarity is estimated by a word embedding model that maps words into an N-dimensional space. In our second approach, we account for the word-ordering within a sentence. We use a sentence encoder to embed GO definitions into vectors and estimate how likely one definition entails another. We validate our methods in two ways. In the first experiment, we test the model’s ability to differentiate a true protein-protein network from a randomly generated network. In the second experiment, we test the model in identifying orthologs from randomly-matched genes in human, mouse, and fly. In both experiments, a hybrid of NLP and GO-tree based method achieves the best classification accuracy.Availabilitygithub.com/datduong/NLPMethods2CompareGOterms

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