Performance Analysis of Machine Learning Based Classifiers for the Diagnosis of Melanoma Cancer and Comparison

2018 ◽  
Vol 15 (2) ◽  
pp. 558-575
Author(s):  
A. Anto Spiritus Kingsly ◽  
B. Sankaragomathi
Keyword(s):  
Detection System ◽  
Noise Removal ◽  
Morphological Processing ◽  
Svm Classifier ◽  
Bag Of Visual Words ◽  
Data Set ◽  
Visual Words ◽  
Melanoma Cancer ◽  
Processing Step ◽  
Ann Classifier

Melanoma cancer is the most injurious form of cancer which affects the human. Skin cancer has quickly increased in western part of the country among the world. In this paper, diagnosing melanoma in premature stages a detection system has been designed which contains the following digital image processing techniques. First, dermoscopy image of skin is taken, and it is subjected to the pre-processing step for noise removal and post-processing step for image enhancement. Then the processed image undergoes image segmentation using Otsu method and Morphological processing. Second, features are extracted using feature extraction technique-ABCD parameter, GLCM, and FOS. Various feature combinations are given as the input to the KNN, SVM, ANN and Bag of Visual Words classifiers. KNN classifier is used to classify the data set into two classes, SVM classifier is used to classify the data set into three classes, ANN classifier is used to classify the data set based on the number of layers and Bag of Visual Words are used to classify the data set into two classes. Performance is analyzed based on the accuracy of the learning classifier output.

scholarly journals Viable Yeast Identification using Bag of Visual Words in Colored images

2020 ◽  
Author(s):  
Junior Souza ◽  
Vanessa Weber ◽  
Ariadne Gonçalves ◽  
Marco Alvarez ◽  
Marney Cereda ◽  
...  
Keyword(s):  
Population Control ◽  
Human Vision ◽  
Svm Classifier ◽  
P Value ◽  
Bag Of Visual Words ◽  
Yeast Identification ◽  
Percent Correct ◽  
Visual Words ◽  
Significant Difference ◽  
Opponent Color

In this research it is reported a system to automate the process of identification of viable yeasts whose population control is a crucial task in the ethanol production process. The identification and counting of yeasts made by human vision under a light microscope, is repetitive and susceptible to errors. We used computer vision techniques such as BoVW, Color Coherence Vectors (CCV), Color Moments (CM), Bag-of-Color (BoC) and Opponent Color (OpC) were applied for extracting characteristics that were classified by the Naive Bayes, KNN, SVM and J48 algorithms in 2614 images of yeasts separated into three classes: viable, non-viable and background. The results were analyzed using software R, which in the ANOVA test resulted in a p value equal to 2e􀀀16 indicating a significant difference between the techniques. The OPC with SVM classifier showed the highest performance using the PCC Percent Correct Classification metric, about 95% compared to other techniques.

scholarly journals Plant Disease Detection System using Bag of Visual Words

2018 ◽  
Vol 10 (9) ◽  
pp. 57-63 ◽  
Author(s):  
D. Asir Antony Gnana Singh ◽  
◽  
E. Jebamalar Leavline ◽  
A. K. Abirami ◽  
M. Dhivya
Keyword(s):  
Plant Disease ◽  
Detection System ◽  
Disease Detection ◽  
Bag Of Visual Words ◽  
Visual Words

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

Faster multi-defect detection system in shield tunnel using combination of FCN and faster RCNN

2019 ◽  
Vol 22 (13) ◽  
pp. 2907-2921 ◽  
Author(s):  
Xinwen Gao ◽  
Ming Jian ◽  
Min Hu ◽  
Mohan Tanniru ◽  
Shuaiqing Li
Keyword(s):  
Boundary Layer ◽  
Defect Detection ◽  
Detection Rate ◽  
Layer Structure ◽  
Detection System ◽  
Identification Accuracy ◽  
Shield Tunnel ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set

With the large-scale construction of urban subways, the detection of tunnel defects becomes particularly important. Due to the complexity of tunnel environment, it is difficult for traditional tunnel defect detection algorithms to detect such defects quickly and accurately. This article presents a deep learning FCN-RCNN model that can detect multiple tunnel defects quickly and accurately. The algorithm uses a Faster RCNN algorithm, Adaptive Border ROI boundary layer and a three-layer structure of the FCN algorithm. The Adaptive Border ROI boundary layer is used to reduce data set redundancy and difficulties in identifying interference during data set creation. The algorithm is compared with single FCN algorithm with no Adaptive Border ROI for different defect types. The results show that our defect detection algorithm not only addresses interference due to segment patching, pipeline smears and obstruction but also the false detection rate decreases from 0.371, 0.285, 0.307 to 0.0502, respectively. Finally, corrected by cylindrical projection model, the false detection rate is further reduced from 0.0502 to 0.0190 and the identification accuracy of water leakage defects is improved.

Object Classification of Aerial Images With Bag-of-Visual Words

2010 ◽  
Vol 7 (2) ◽  
pp. 366-370 ◽  
Author(s):  
Sheng Xu ◽  
Tao Fang ◽  
Deren Li ◽  
Shiwei Wang
Keyword(s):  
Object Classification ◽  
Aerial Images ◽  
Bag Of Visual Words ◽  
Visual Words
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