Artificial Intelligence and Image Analysis for the Identification of Endometrial Malignancies

The aim of this study is to compare machine learning algorithms (MLAs) in the discrimination between benign and malignant endometrial nuclei and lesions. Nuclei characteristics are obtained via image analysis and were measured from liquid-based cytology slides. Four hundred sixteen histologically confirmed patients were involved, 168 healthy, and the remaining with pathological endometrium. Fifty percent of the cases were used to three MLAs: a feedforward artificial neural network (ANN) trained by the backpropagation algorithm, a learning vector quantization (LVQ), and a competitive learning ANN. The outcome of this process was the classification of cell nuclei as benign or malignant. Based on the nuclei classification, an algorithm to classify individual patients was constructed. The sensitivity of the MLAs in training set for nuclei classification was in the range of 77%-84%. Patients' classification had sensitivity in the range of 90%-98%. These findings indicate that MLAs have good performance for the classification of endometrial nuclei and lesions.

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Artificial Intelligence for the Identification of Endometrial Malignancies

Natural Language Processing ◽

10.4018/978-1-7998-0951-7.ch014 ◽

2020 ◽

pp. 266-279

Author(s):

Abraham Pouliakis ◽

Niki Margari ◽

Effrosyni Karakitsou ◽

Evangelia Alamanou ◽

Nikolaos Koureas ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Image Analysis ◽

Vector Quantization ◽

System Performance ◽

Morphometric Characteristics ◽

Cell Nuclei ◽

Performance Indices ◽

And Performance

Aim of this article is to investigate the potential of Artificial Intelligence (AI) in the discrimination between benign and malignant endometrial nuclei and lesions. For this purpose, 416 histologically confirmed liquid-based cytological smears were collected and morphometric characteristics of cell nuclei were measured via image analysis. Then, 50% of the cases were used to train an AI system, specifically a learning vector quantization (LVQ) neural network. As a result, cell nuclei were classified as benign or malignant. Data from the remaining 50% of the cases were used to evaluate the AI system performance. By nucleic classification, an algorithm for the classification of individual patients was constructed, and performance indices on patient classification were calculated. The sensitivity for the classification of nuclei was 77.95%, and the specificity was 73.93%. For the classification of individual patients, the sensitivity was 90.70% and the specificity 82.79%. These results indicate that an AI system can have an important role in endometrial lesions classification.

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Artificial Intelligence for the Identification of Endometrial Malignancies

International Journal of Reliable and Quality E-Healthcare ◽

10.4018/ijrqeh.2018040103 ◽

2018 ◽

Vol 7 (2) ◽

pp. 37-50 ◽

Cited By ~ 5

Author(s):

Abraham Pouliakis ◽

Niki Margari ◽

Effrosyni Karakitsou ◽

Evangelia Alamanou ◽

Nikolaos Koureas ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Vector Quantization ◽

System Performance ◽

Patient Classification ◽

Morphometric Characteristics ◽

Cell Nuclei ◽

Performance Indices ◽

And Performance

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Implementation of Artificial Intelligence for Classification of Frogs in Bioacoustics

Symmetry ◽

10.3390/sym11121454 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1454 ◽

Cited By ~ 2

Author(s):

Kuo-Wei Chao ◽

Nian-Ze Hu ◽

Yi-Chu Chao ◽

Chin-Kai Su ◽

Wei-Hang Chiu

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Machine Learning ◽

Spectral Feature ◽

Machine Learning Algorithms ◽

Learning Performance ◽

Support Vector ◽

Processing Unit ◽

Raw Data

This research presents the implementation of artificial intelligence (AI) for classification of frogs in symmetry of the bioacoustics spectral by using the feedforward neural network approach (FNNA) and support vector machine (SVM). Recently, the symmetry concept has been applied in physics, and in mathematics to help make mathematical models tractable to achieve the best learning performance. Owing to the symmetry of the bioacoustics spectral, feature extraction can be achieved by integrating the techniques of Mel-scale frequency cepstral coefficient (MFCC) and mentioned machine learning algorithms, such as SVM, neural network, and so on. At the beginning, the raw data information for our experiment is taken from a website which collects many kinds of frog sounds. This in fact saves us collecting the raw data by using a digital signal processing technique. The generally proposed system detects bioacoustic features by using the microphone sensor to record the sounds of different frogs. The data acquisition system uses an embedded controller and a dynamic signal module for making high-accuracy measurements. With regard to bioacoustic features, they are filtered through the MFCC algorithm. As the filtering process is finished, all values from ceptrum signals are collected to form the datasets. For classification and identification of frogs, we adopt the multi-layer FNNA algorithm in machine learning and the results are compared with those obtained by the SVM method at the same time. Additionally, two optimizer functions in neural network include: scaled conjugate gradient (SCG) and gradient descent adaptive learning rate (GDA). Both optimization methods are used to evaluate the classification results from the feature datasets in model training. Also, calculation results from the general central processing unit (CPU) and Nvidia graphics processing unit (GPU) processors are evaluated and discussed. The effectiveness of the experimental system on the filtered feature datasets is classified by using the FNNA and the SVM scheme. The expected experimental results of the identification with respect to different symmetry bioacoustic features of fifteen frogs are obtained and finally distinguished.

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MACHINE LEARNING ALGORITHMS FOR IDENTIFICATION OF ABNORMAL GLOW CURVES AND ASSOCIATED ABNORMALITY IN CaSO4:DY-BASED PERSONNEL MONITORING DOSIMETERS

Radiation Protection Dosimetry ◽

10.1093/rpd/ncaa108 ◽

2020 ◽

Vol 190 (3) ◽

pp. 342-351

Author(s):

Munir S Pathan ◽

S M Pradhan ◽

T Palani Selvam

Keyword(s):

Machine Learning ◽

Glow Curve ◽

Good Accuracy ◽

Machine Learning Algorithms ◽

Support Vector ◽

Computationally Efficient ◽

Artificial Neural Network Ann ◽

First Time

Abstract In the present study, machine learning (ML) methods for the identification of abnormal glow curves (GC) of CaSO4:Dy-based thermoluminescence dosimeters in individual monitoring are presented. The classifier algorithms, random forest (RF), artificial neural network (ANN) and support vector machine (SVM) are employed for identifying not only the abnormal glow curve but also the type of abnormality. For the first time, the simplest and computationally efficient algorithm based on RF is presented for GC classifications. About 4000 GCs are used for the training and validation of ML algorithms. The performance of all algorithms is compared by using various parameters. Results show a fairly good accuracy of 99.05% for the classification of GCs by RF algorithm. Whereas 96.7% and 96.1% accuracy is achieved using ANN and SVM, respectively. The RF-based classifier is recommended for GC classification as well as in assisting the fault determination of the TLD reader system.

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A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery

Remote Sensing of Environment ◽

10.1016/j.rse.2011.11.020 ◽

2012 ◽

Vol 118 ◽

pp. 259-272 ◽

Cited By ~ 453

Author(s):

Dennis C. Duro ◽

Steven E. Franklin ◽

Monique G. Dubé

Keyword(s):

Machine Learning ◽

Image Analysis ◽

Learning Algorithms ◽

Agricultural Landscapes ◽

Machine Learning Algorithms ◽

Object Based Image Analysis ◽

Object Based ◽

Spot 5

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Computational classification of animals for a highway detection system

Brazilian Journal of Veterinary Research and Animal Science ◽

10.11606/issn.1678-4456.bjvras.2021.174951 ◽

2021 ◽

Vol 58 ◽

pp. e174951

Author(s):

Denis Sato ◽

Adroaldo José Zanella ◽

Ernane Xavier Costa

Keyword(s):

Neural Network ◽

Machine Learning ◽

Computer Vision ◽

Convolutional Neural Network ◽

Road Safety ◽

Detection System ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

The World

Vehicle-animal collisions represent a serious problem in roadway infrastructure. To avoid these roadway collisions, different mitigation systems have been applied in various regions of the world. In this article, a system for detecting animals on highways is presented using computer vision and machine learning algorithms. The models were trained to classify two groups of animals: capybaras and donkeys. Two variants of the convolutional neural network called Yolo (You only look once) were used, Yolov4 and Yolov4-tiny (a lighter version of the network). The training was carried out using pre-trained models. Detection tests were performed on 147 images. The accuracy results obtained were 84.87% and 79.87% for Yolov4 and Yolov4-tiny, respectively. The proposed system has the potential to improve road safety by reducing or preventing accidents with animals.

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Classification of masked image data

PLoS ONE ◽

10.1371/journal.pone.0254181 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254181

Author(s):

Kamila Lis ◽

Mateusz Koryciński ◽

Konrad A. Ciecierski

Keyword(s):

Neural Network ◽

Machine Learning ◽

Image Data ◽

Original Data ◽

Machine Learning Algorithms ◽

General Data Protection Regulation ◽

Additional Information ◽

Classification Of Images ◽

Applications Of Machine Learning

Data classification is one of the most commonly used applications of machine learning. The are many developed algorithms that can work in various environments and for different data distributions that perform this task with excellence. Classification algorithms, just like other machine learning algorithms have one thing in common: in order to operate on data, they must see the data. In the present world, where concerns about privacy, GDPR (General Data Protection Regulation), business confidentiality and security are growing bigger and bigger; this requirement to work directly on the original data might become, in some situations, a burden. In this paper, an approach to the classification of images that cannot be directly accessed during training has been made. It has been shown that one can train a deep neural network to create such a representation of the original data that i) without additional information, the original data cannot be restored, and ii) that this representation—called a masked form—can still be used for classification purposes. Moreover, it has been shown that classification of the masked data can be done using both classical and neural network-based classifiers.

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Relevance of Machine Learning to Cardiovascular Imaging

Deep Learning Applications in Medical Imaging - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-7998-5071-7.ch003 ◽

2021 ◽

pp. 78-99

Author(s):

Sumesh Sasidharan ◽

M. Yousuf Salmasi ◽

Selene Pirola ◽

Omar A. Jarral

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Image Analysis ◽

Cognitive Learning ◽

Cardiovascular Imaging ◽

Machine Learning Algorithms ◽

Patient Specific ◽

Healthcare Data ◽

Statistical Approaches ◽

Specific Management

Artificial intelligence (AI) broadly concerns analytical algorithms that iteratively learn from big datasets, allowing computers to find concealed insights. These encompass a range of operations comprising several terms, including machine learning(ML), cognitive learning, deep learning, and reinforcement learning-based methods that can be used to incorporate and comprehend complex biomedical and healthcare data in scenarios where traditional statistical approaches cannot be implemented. For cardiovascular imaging in particular, machine learning guarantees to be a transformative tool that can address many unmet needs for patient-specific management, accurate prediction of disease progression, and the tracking of identifiable biomarkers of disease processes. In this chapter, the authors discuss fundamentals of machine learning algorithms for image analysis in the cardiovascular system by evaluating the need for ML in this field and examining the potential obstacles and challenges of implementation in the context of three common imaging modalities used in cardiovascular medicine.

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ML-PASD

Advances in Medical Diagnosis, Treatment, and Care - Artificial Intelligence for Accurate Analysis and Detection of Autism Spectrum Disorder ◽

10.4018/978-1-7998-7460-7.ch006 ◽

2021 ◽

pp. 82-93

Author(s):

Vishal Jagota ◽

Vinay Bhatia ◽

Luis Vives ◽

Arun B. Prasad

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Machine Learning ◽

Autism Spectrum Disorder ◽

Artificial Neural Network ◽

Early Stage ◽

Age Groups ◽

Autism Spectrum ◽

Screening Procedures ◽

Artificial Neural Network Ann

Autism spectrum disorder (ASD) is growing faster than ever before. Autism detection is costly and time intensive with screening procedures. Autism can be detected at an early stage by the development of artificial intelligence and machine learning (ML). While a number of experiments using many approaches were conducted, these studies provided no conclusion as to the prediction of autism characteristics in various age groups. This chapter is therefore intended to suggest an accurate MLASD predictive model based on the ML methodology to prevent ASD for people of all ages. It is a method for prediction. This survey was conducted to develop and assess ASD prediction in an artificial neural network (ANN). AQ-10 data collection was used to test the proposed pattern. The findings of the evaluation reveal that the proposed prediction model has improved results in terms of consistency, specificity, sensitivity, and dataset accuracy.

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Video-Based Human Activity Recognition for Elderly Using Convolutional Neural Network

International Journal of Security and Privacy in Pervasive Computing ◽

10.4018/ijsppc.2020010104 ◽

2020 ◽

Vol 12 (1) ◽

pp. 36-48

Author(s):

Vijayaprabakaran K. ◽

Sathiyamurthy K. ◽

Ponniamma M.

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Image Classification ◽

Elderly People ◽

Activity Recognition ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

Human Vision

A typical healthcare application for elderly people involves monitoring daily activities and providing them with assistance. Automatic analysis and classification of an image by the system is difficult compared to human vision. Several challenging problems for activity recognition from the surveillance video involving the complexity of the scene analysis under observations from irregular lighting and low-quality frames. In this article, the authors system use machine learning algorithms to improve the accuracy of activity recognition. Their system presents a convolutional neural network (CNN), a machine learning algorithm being used for image classification. This system aims to recognize and assist human activities for elderly people using input surveillance videos. The RGB image in the dataset used for training purposes which requires more computational power for classification of the image. By using the CNN network for image classification, the authors obtain a 79.94% accuracy in the experimental part which shows their model obtains good accuracy for image classification when compared with other pre-trained models.

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