scholarly journals The evaluation of synthetic datasets on training AlexNet for surgical tool detection

2020 ◽  
Vol 6 (3) ◽  
pp. 319-321
Author(s):  
N. Ding ◽  
N. A. Jalal ◽  
T. A. Alshirbaji ◽  
K. Möller
Keyword(s):  
Data Augmentation ◽  
Correct Classification ◽  
Imbalanced Dataset ◽  
Test Set ◽  
Surgical Workflow ◽  
Performance Training ◽  
Surgical Tool ◽  
Synthetic Datasets ◽  
Laparoscopic Videos ◽  
Problem Data

AbstractSurgical tool recognition is a key task to analyze surgical workflow, in order to improve the efficiency and safety of laparoscopic surgeries. The laparoscopic videos are important sources to conduct this task, However, there are some challenges to analyze these videos. Focus on the imbalanced dataset problem, data augmentation method based on generate different synthetic datasets and evaluate their performance training on a convolutional neural network model are investigated in this research. The results show the effect on the model with different background patterns. A better performance was achieved when the model was trained by a structure background dataset. Further research will be needed to understand why the original background patterns support the correct classification. It is assumed that this is an overlearning effect, that will not hold if other procedures were included into the test set.

scholarly journals A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3726
Author(s):  
Ivan Vaccari ◽  
Vanessa Orani ◽  
Alessia Paglialonga ◽  
Enrico Cambiaso ◽  
Maurizio Mongelli
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Monitoring Program ◽  
Clinical Decision Support Systems ◽  
Direct Access ◽  
Generative Adversarial Networks ◽  
Chronic Obstructive ◽  
Generative Adversarial Network ◽  
Internet Of Medical Things ◽  
Synthetic Datasets

The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the “Internet of Medical Things” (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. For this reason, data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models. In this work, we apply the concept of generative adversarial networks (GANs) to perform a data augmentation from patient data obtained through IoMT sensors for Chronic Obstructive Pulmonary Disease (COPD) monitoring. We also apply an explainable AI algorithm to demonstrate the accuracy of the synthetic data by comparing it to the real data recorded by the sensors. The results obtained demonstrate how synthetic datasets created through a well-structured GAN are comparable with a real dataset, as validated by a novel approach based on machine learning.

scholarly journals The Use of Synthetic Data to Facilitate Eye Segmentation Using Deeplabv3+

2021 ◽  
Vol 5 (3) ◽  
pp. 1-10
Author(s):  
Melih Öz ◽  
Taner Danışman ◽  
Melih Günay ◽  
Esra Zekiye Şanal ◽  
Özgür Duman ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation ◽  
Real Life ◽  
Synthetic Data ◽  
Limited Data ◽  
Test Set ◽  
Human Eye ◽  
Context Data ◽  
Head Positions

The human eye contains valuable information about an individual’s identity and health. Therefore, segmenting the eye into distinct regions is an essential step towards gathering this useful information precisely. The main challenges in segmenting the human eye include low light conditions, reflections on the eye, variations in the eyelid, and head positions that make an eye image hard to segment. For this reason, there is a need for deep neural networks, which are preferred due to their success in segmentation problems. However, deep neural networks need a large amount of manually annotated data to be trained. Manual annotation is a labor-intensive task, and to tackle this problem, we used data augmentation methods to improve synthetic data. In this paper, we detail the exploration of the scenario, which, with limited data, whether performance can be enhanced using similar context data with image augmentation methods. Our training and test set consists of 3D synthetic eye images generated from the UnityEyes application and manually annotated real-life eye images, respectively. We examined the effect of using synthetic eye images with the Deeplabv3+ network in different conditions using image augmentation methods on the synthetic data. According to our experiments, the network trained with processed synthetic images beside real-life images produced better mIoU results than the network, which only trained with real-life images in the Base dataset. We also observed mIoU increase in the test set we created from MICHE II competition images.

Oversampling Based on Data Augmentation in Convolutional Neural Network for Silicon Wafer Defect Classification

2020 ◽  
Author(s):  
Uzma Batool ◽  
Mohd Ibrahim Shapiai ◽  
Nordinah Ismail ◽  
Hilman Fauzi ◽  
Syahrizal Salleh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Silicon Wafer ◽  
Data Augmentation ◽  
Imbalanced Data ◽  
Training Data ◽  
Defect Classification ◽  
Learning Method ◽  
Test Set

Silicon wafer defect data collected from fabrication facilities is intrinsically imbalanced because of the variable frequencies of defect types. Frequently occurring types will have more influence on the classification predictions if a model gets trained on such skewed data. A fair classifier for such imbalanced data requires a mechanism to deal with type imbalance in order to avoid biased results. This study has proposed a convolutional neural network for wafer map defect classification, employing oversampling as an imbalance addressing technique. To have an equal participation of all classes in the classifier’s training, data augmentation has been employed, generating more samples in minor classes. The proposed deep learning method has been evaluated on a real wafer map defect dataset and its classification results on the test set returned a 97.91% accuracy. The results were compared with another deep learning based auto-encoder model demonstrating the proposed method, a potential approach for silicon wafer defect classification that needs to be investigated further for its robustness.

scholarly journals Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

2021 ◽  
Vol 9 ◽  
Author(s):  
Shui-Hua Wang ◽  
Suresh Chandra Satapathy ◽  
Donovan Anderson ◽  
Shi-Xin Chen ◽  
Yu-Dong Zhang
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
State Of The Art ◽  
Model Averaging ◽  
Community Acquired Pneumonia ◽  
Test Set ◽  
Max Pooling ◽  
L2 Norm ◽  
Healthy Control ◽  
Novel Model

Aim: Coronavirus disease 2019 (COVID-19) is a form of disease triggered by a new strain of coronavirus. This paper proposes a novel model termed “deep fractional max pooling neural network (DFMPNN)” to diagnose COVID-19 more efficiently.Methods: This 12-layer DFMPNN replaces max pooling (MP) and average pooling (AP) in ordinary neural networks with the help of a novel pooling method called “fractional max-pooling” (FMP). In addition, multiple-way data augmentation (DA) is employed to reduce overfitting. Model averaging (MA) is used to reduce randomness.Results: We ran our algorithm on a four-category dataset that contained COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis (SPT), and healthy control (HC). The 10 runs on the test set show that the micro-averaged F1 (MAF) score of our DFMPNN is 95.88%.Discussions: This proposed DFMPNN is superior to 10 state-of-the-art models. Besides, FMP outperforms traditional MP, AP, and L2-norm pooling (L2P).

scholarly journals Towards more efficient CNN-based surgical tools classification using transfer learning

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jaafar Jaafari ◽  
Samira Douzi ◽  
Khadija Douzi ◽  
Badr Hssina
Keyword(s):  
Data Augmentation ◽  
Context Aware ◽  
Average Precision ◽  
Surgical Quality ◽  
Multiple Data ◽  
Surgical Tool ◽  
Augmentation Techniques ◽  
Automatic Tool ◽  
Surgical Teams ◽  
Context Aware System

AbstractContext-aware system (CAS) is a system that can understand the context of a given situation and either share this context with other systems for their response or respond by itself. In surgery, these systems are intended to assist surgeons enhance the scheduling productivity of operating rooms (OR) and surgical teams, and promote a comprehensive perception and consciousness of the OR. Furthermore, the automated surgical tool classification in medical images is a real-time computerized assistance to the surgeons in conducting different operations. Moreover, deep learning has embroiled in every facet of life due to the availability of large datasets and the emergence of convolutional neural networks (CNN) that have paved the way for the development of different image related processes. The aim of this paper is to resolve the problem of unbalanced data in the publicly available Cholec80 laparoscopy video dataset, using multiple data augmentation techniques. Furthermore, we implement a fine-tuned CNN to tackle the automatic tool detection during a surgery, with prospective use in the teaching field, evaluating surgeons, and surgical quality assessment (SQA). The proposed method is evaluated on a dataset of 80 cholecystectomy videos (Cholec80 dataset). A mean average precision of 93.75% demonstrates the effectiveness of the proposed method, outperforming the other models significantly.

Multi-class deep learning segmentation and automated measurements in periodontal sonograms of a porcine model

2021 ◽  
Author(s):  
Ying-Chun Pan ◽  
Hsun-Liang Chan ◽  
Xiangbo Kong ◽  
Lubomir M. Hadjiiski ◽  
Oliver D. Kripfgans
Keyword(s):  
Deep Learning ◽  
Soft Tissue ◽  
Data Augmentation ◽  
Porcine Model ◽  
Dice Similarity Coefficient ◽  
Test Set ◽  
Expert Annotation ◽  
Novice Users ◽  
Test Sets ◽  
Class Segmentation

Objectives: Ultrasound emerges as a complement to cone-beam computed tomography in dentistry, but struggles with artifacts like reverberation and shadowing. This study seeks to help novice users recognize soft tissue, bone, and crown of a dental sonogram, and automate soft tissue height (STH) measurement using deep learning. Methods: In this retrospective study, 627 frames from 111 independent cine loops of mandibular and maxillary premolar and incisors collected from our porcine model (N = 8) were labeled by a reader. 274 premolar sonograms, including data augmentation, were used to train a multi class segmentation model. The model was evaluated against several test sets, including premolar of the same breed (n = 74, Yucatan) and premolar of a different breed (n = 120, Sinclair). We further proposed a rule-based algorithm to automate STH measurements using predicted segmentation masks. Results: The model reached a Dice similarity coefficient of 90.7±4.39%, 89.4±4.63%, and 83.7±10.5% for soft tissue, bone, and crown segmentation, respectively on the first test set (n = 74), and 90.0±7.16%, 78.6±13.2%, and 62.6±17.7% on the second test set (n = 120). The automated STH measurements have a mean difference (95% confidence interval) of −0.22 mm (−1.4, 0.95), a limit of agreement of 1.2 mm, and a minimum ICC of 0.915 (0.857, 0.948) when compared to expert annotation. Conclusion: This work demonstrates the potential use of deep learning in identifying periodontal structures on sonograms and obtaining diagnostic periodontal dimensions.

scholarly journals Identification of Women for Referral to Colposcopy by Neural Networks: A Preliminary Study Based on LBC and Molecular Biomarkers

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Petros Karakitsos ◽  
Charalampos Chrelias ◽  
Abraham Pouliakis ◽  
George Koliopoulos ◽  
Aris Spathis ◽  
...  
Keyword(s):  
Precancerous Lesions ◽  
Correct Classification ◽  
Test Set ◽  
Dna Test ◽  
Hpv Dna ◽  
Vector Quantizer ◽  
Cervical Precancerous Lesions ◽  
Liquid Based Cytology ◽  
Preliminary Study

Objective of this study is to investigate the potential of the learning vector quantizer neural network (LVQ-NN) classifier on various diagnostic variables used in the modern cytopathology laboratory and to build an algorithm that may facilitate the classification of individual cases. From all women included in the study, a liquid-based cytology sample was obtained; this was tested via HPV DNA test, E6/E7 HPV mRNA test, and p16 immunostaining. The data were classified by the LVQ-NN into two groups: CIN-2 or worse and CIN-1 or less. Half of the cases were used to train the LVQ-NN; the remaining cases (test set) were used for validation. Out of the 1258 cases, cytology identified correctly 72.90% of the CIN-2 or worst cases and 97.37% of the CIN-1 or less cases, with overall accuracy 94.36%. The application of the LVQ-NN on the test set allowed correct classification for 84.62% of the cases with CIN-2 or worse and 97.64% of the cases with CIN-1 or less, with overall accuracy of 96.03%. The use of the LVQ-NN with cytology and the proposed biomarkers improves significantly the correct classification of cervical precancerous lesions and/or cancer and may facilitate diagnosis and patient management.

scholarly journals A convolutional neural network with a two-stage LSTM model for tool presence detection in laparoscopic videos

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Tamer Abdulbaki Alshirbaji ◽  
Nour Aldeen Jalal ◽  
Knut Möller
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Critical Role ◽  
Video Clip ◽  
Short Term ◽  
Two Stage ◽  
Surgical Tool ◽  
Long Short Term Memory ◽  
Laparoscopic Videos

AbstractSurgical tool presence detection in laparoscopic videos is a challenging problem that plays a critical role in developing context-aware systems in operating rooms (ORs). In this work, we propose a deep learning-based approach for detecting surgical tools in laparoscopic images using a convolutional neural network (CNN) in combination with two long short-term memory (LSTM) models. A pre-trained CNN model was trained to learn visual features from images. Then, LSTM was employed to include temporal information through a video clip of neighbour frames. Finally, the second LSTM was utilized to model temporal dependencies across the whole surgical video. Experimental evaluation has been conducted with the Cholec80 dataset to validate our approach. Results show that the most notable improvement is achieved after employing the two-stage LSTM model, and the proposed approach achieved better or similar performance compared with state-of-the-art methods.

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