Artificial intelligent classification of biomedical color image using quaternion discrete radial Tchebichef moments

2020 ◽  
Author(s):  
Hicham Amakdouf ◽  
Amal Zouhri ◽  
Mostafa El Mallahi ◽  
Ahmed Tahiri ◽  
Driss Chenouni ◽  
...  
Keyword(s):  
Color Image ◽  
Artificial Intelligent ◽  
Tchebichef Moments

scholarly journals Classification of older adults undergoing two dual-task training protocols based on artificial intelligent methods

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Rebeca Sarai ◽  
Francis Trombini-Souza ◽  
Vitoria Thaysa Gomes De Moura ◽  
Rafael Caldas ◽  
Fernando Buarque
Keyword(s):  
Older Adults ◽  
Dual Task ◽  
Artificial Intelligent ◽  
Task Training ◽  
Dual Task Training ◽  
Intelligent Methods ◽  
Training Protocols

Intelligent Classification of Palm Oil Tree Pollination Using E-Nose

Mekatronika ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 28-37
Author(s):  
Abdul Rahim Jalil ◽  
Muhammad Sharfi Najib ◽  
Suhaimi Mohd Daud ◽  
Mujahid Mohamad
Keyword(s):  
Palm Oil ◽  
Case Based Reasoning ◽  
Artificial Intelligent ◽  
Male And Female ◽  
Intelligent Technique ◽  
Insect Activity ◽  
Artificial Intelligent Technique ◽  
Case Based ◽  
Odour Profile

The pollination period is one of the crucial steps needed to ensure crop yield increases, especially in palm oil palm plantations. Most of the research has difficulty determining the pollination period of palm oil. Many problems contribute to this problem, such as difficut to reach and depedency of the polination insect as the insect activity is influenced by the surrounding enviroment.E-Nose can help determine the period by classifiy odour pattern of the male and female palm oil flower. The pattern of each of the flowers were classified using cased – based reasoning artificial intelligent technique. This paper shows the research of the palm oil pollination flower odour profile pattern using case-based reasoning (CBR) classifier.

scholarly journals A PRELIMARY APPROACH FOR THE AUTOMATED RECOGNITION OF MALIGNANT MELANOMA

2011 ◽  
Vol 23 (2) ◽  
pp. 121 ◽  
Author(s):  
Ezzeddine Zagrouba ◽  
Walid Barhoumi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Color Image ◽  
Skin Lesions ◽  
Benign Lesions ◽  
Data Set ◽  
Pigmented Lesions ◽  
Artificial Neural Network Classifier ◽  
Artificial Neural

In this work, we are motivated by the desire to classify skin lesions as malignants or benigns from color photographic slides of the lesions. Thus, we use color images of skin lesions, image processing techniques and artificial neural network classifier to distinguish melanoma from benign pigmented lesions. As the first step of the data set analysis, a preprocessing sequence is implemented to remove noise and undesired structures from the color image. Second, an automated segmentation approach localizes suspicious lesion regions by region growing after a preliminary step based on fuzzy sets. Then, we rely on quantitative image analysis to measure a series of candidate attributes hoped to contain enough information to differentiate melanomas from benign lesions. At last, the selected features are supplied to an artificial neural network for classification of tumor lesion as malignant or benign. For a preliminary balanced training/testing set, our approach is able to obtain 79.1% of correct classification of malignant and benign lesions on real skin lesion images.

A CNN-Based Mosquito Classification Using Image Transformation of Wingbeat Features

2020 ◽  
Author(s):  
Jose Alvaro Luna-Gonzalez ◽  
Daniel Robles-Camarillo ◽  
Mariko Nakano-Miyatake ◽  
Humberto Lanz-Mendoza ◽  
Hector Perez-Meana
Keyword(s):  
Anopheles Arabiensis ◽  
World Wide ◽  
Color Image ◽  
Audio Features ◽  
Audio Feature ◽  
Representative Species ◽  
Audio Feature Extraction ◽  
Feature Values ◽  
Fully Connected

In this paper, a classification of mosquito’s specie is performed using mosquito wingbeats samples obtained by optical sensor. Six world-wide representative species of mosquitos, which are Aedes aegypti, Aedes albopictus, Anopheles arabiensis, Anopheles gambiae and Culex pipiens, Culex quinquefasciatus, are considered for classification. A total of 60,000 samples are divided equally in each specie mentioned above. In total, 25 audio feature extraction algorithms are applied to extract 39 feature values per sample. Further, each audio feature is transformed to a color image, which shows audio features presenting by different pixel values. We used a fully connected neural networks for audio features and a convolutional neural network (CNN) for image dataset generated from audio features. The CNN-based classifier shows 90.75% accuracy, which outperforms the accuracy of 87.18% obtained by the first classifier using directly audio features.

scholarly journals Myoglobin-Based Classification of Minced Meat Using Hyperspectral Imaging

2020 ◽  
Vol 10 (19) ◽  
pp. 6862 ◽  
Author(s):  
Hamail Ayaz ◽  
Muhammad Ahmad ◽  
Ahmed Sohaib ◽  
Muhammad Naveed Yasir ◽  
Martha A. Zaidan ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Human Error ◽  
Color Image ◽  
Average Accuracy ◽  
True Color Image ◽  
Filtering Technique ◽  
True Color ◽  
Minced Meat ◽  
Non Destructive

Minced meat substitution is one of the most common frauds which not only affects consumer health but impacts their lifestyles and religious customs as well. A number of methods have been proposed to overcome these frauds; however, these mostly rely on laboratory measures and are often subject to human error. Therefore, this study proposes novel hyperspectral imaging (400–1000 nm) based non-destructive isos-bestic myoglobin (Mb) spectral features for minced meat classification. A total of 60 minced meat spectral cubes were pre-processed using true-color image formulation to extract regions of interest, which were further normalized using the Savitzky–Golay filtering technique. The proposed pipeline outperformed several state-of-the-art methods by achieving an average accuracy of 88.88%.

Binary and Multiclass Classification of Histopathological Images Using Machine Learning Techniques

2020 ◽  
Vol 10 (9) ◽  
pp. 2252-2258
Author(s):  
Jiatong Wang ◽  
Tiantian Zhu ◽  
Shan Liang ◽  
R. Karthiga ◽  
K. Narasimhan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Color Image ◽  
Multiclass Classification ◽  
Automated System ◽  
Machine Learning Techniques ◽  
Tubular Adenoma ◽  
Magnification Factor ◽  
Tree Classifier ◽  
Histopathological Images

Background and Objective: Breast cancer is fairly common and widespread form of cancer among women. Digital mammogram, thermal images of breast and digital histopathological images serve as a major tool for the diagnosis and grading of cancer. In this paper, a novel attempt has been proposed using image analysis and machine learning algorithm to develop an automated system for the diagnosis and grading of cancer. Methods: BreaKHis dataset is employed for the present work where images are available with different magnification factor namely 40×, 100×, 200×, 400× and 200× magnification factor is utilized for the present work. Accurate preprocessing steps and precise segmentation of nuclei in histopathology image is a necessary prerequisite for building an automated system. In this work, 103 images from benign and 103 malignant images are used. Initially color image is reshaped to gray scale format by applying Otsu thresholding, followed by top hat, bottom hat transform in preprocessing stage. The threshold value selected based on Ridler and calvard algorithm, extended minima transform and median filtering is applied for doing further steps in preprocessing. For segmentation of nuclei distance transform and watershed are used. Finally, for feature extraction, two different methods are explored. Result: In binary classification benign and malignant classification is done with the highest accuracy rate of 89.7% using ensemble bagged tree classifier. In case of multiclass classification 5-class are taken which are adenosis, fibro adenoma, tubular adenoma, mucinous carcinoma and papillary carcinoma the combination of multiclass classification gives the accuracy of 88.1% using ensemble subspace discriminant classifier. To the best of author’s knowledge, it is the first made in a novel attempt made for binary and multiclass classification of histopathology images. Conclusion: By using ensemble bagged tree and ensemble subspace discriminant classifiers the proposed method is efficient and outperform the state of art method in the literature.

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