scholarly journals Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shi Wang ◽  
Melika Hamian
Keyword(s):  
Region Of Interest ◽  
Special Equipment ◽  
Thermal Exchange ◽  
Pigmented Lesions ◽  
Otsu Method ◽  
Advanced Stages ◽  
New Feature ◽  
Learning Machine ◽  
Almost All ◽  
Skin Cancer Detection

Melanoma is defined as a disease that has been incurable in advanced stages, which shows the vital importance of timely diagnosis and treatment. To diagnose this type of cancer early, various methods and equipment have been used, almost all of which required a visit to the doctor and were not available to the public. In this study, an automated and accurate process to differentiate between benign skin pigmented lesions and malignant melanoma is presented, so that it can be used by the general public, and it does not require special equipment and special conditions in imaging. In this study, after preprocessing of the input images, the region of interest is segmented based on the Otsu method. Then, a new feature extraction is implemented on the segmented image to mine the beneficial characteristics. The process is then finalized by using an optimized Deep Believe Network (DBN) for categorization into 2 classes of normal and melanoma cases. The optimization process in DBN has been performed by a developed version of the newly introduced Thermal Exchange Optimization (dTEO) algorithm to obtain higher efficacy in different terms. To show the method’s superiority, its performance is compared with 7 different techniques from the literature.

scholarly journals An Optimized Method for Skin Cancer Diagnosis Using Modified Thermal Exchange Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liu Wei ◽  
Su Xiao Pan ◽  
Y. A. Nanehkaran ◽  
V. Rajinikanth
Keyword(s):  
Skin Cancer ◽  
Optimization Algorithm ◽  
Region Of Interest ◽  
Reduction Process ◽  
The Body ◽  
Disease Treatment ◽  
Cancer Society ◽  
Thermal Exchange ◽  
Otsu Method ◽  
Automatic Pipeline

Skin cancer is the most common cancer of the body. It is estimated that more than one million people worldwide develop skin cancer each year. Early detection of this cancer has a high effect on the disease treatment. In this paper, a new optimal and automatic pipeline approach has been proposed for the diagnosis of this disease from dermoscopy images. The proposed method includes a noise reduction process before processing for eliminating the noises. Then, the Otsu method as one of the widely used thresholding method is used to characterize the region of interest. Afterward, 20 different features are extracted from the image. To reduce the method complexity, a new modified version of the Thermal Exchange Optimization Algorithm is performed to the features. This improves the method precision and consistency. To validate the proposed method’s efficiency, it is implemented to the American Cancer Society database, its results are compared with some state-of-the-art methods, and the final results showed the superiority of the proposed method against the others.

An Improved Method for CT Image Coding Using PSNR Prediction Model Based on ELM

2015 ◽  
Vol 738-739 ◽  
pp. 598-601
Author(s):  
Han Yang Zhu ◽  
Xin Yu Jin ◽  
Jian Feng Shen
Keyword(s):  
Computational Complexity ◽  
Prediction Model ◽  
Image Coding ◽  
Region Of Interest ◽  
Image Encoding ◽  
Large Size ◽  
Learning Machine ◽  
Intractable Problem ◽  
Encoding Efficiency ◽  
Better Than

In telemedicine, medical images are always considered very important telemedicine diagnostic evidences. High transmission delay in a bandwidth limited network becomes an intractable problem because of its large size. It’s important to achieve a quality balance between Region of Interest (ROI) and Background Region (BR) when ROI-based image encoding is being used. In this paper, a research made on balancing method of LS-SVM based ROI/BR PSNR prediction model to optimize the ROI encoding shows it’s much better than conventional methods but with very high computational complexity. We propose a new method using extreme learning machine (ELM) with lower computational complexity to improve encoding efficiency compared to LS-SVM based model. Besides, it also achieves the same effect of balancing ROI and BR.

scholarly journals Region-of-Interest Based Transfer Learning Assisted Framework for Skin Cancer Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 147858-147871
Author(s):  
Rehan Ashraf ◽  
Sitara Afzal ◽  
Attiq Ur Rehman ◽  
Sarah Gul ◽  
Junaid Baber ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Transfer Learning ◽  
Cancer Detection ◽  
Region Of Interest ◽  
Skin Cancer Detection

NONLINEAR DYNAMICS OF A CLASS OF ANALOG-TO-DIGITAL CONVERTERS

1992 ◽  
Vol 02 (02) ◽  
pp. 325-340 ◽  
Author(s):  
ORLA FEELY ◽  
LEON O. CHUA
Keyword(s):  
Region Of Interest ◽  
Self Similarity ◽  
Globally Asymptotically Stable ◽  
Sigma Delta ◽  
Analog To Digital ◽  
Sigma Delta Modulators ◽  
Stable Periodic ◽  
The One ◽  
Almost All ◽  
Constant Slope

Oversampled sigma-delta modulators are finding widespread use in audio and other signal processing applications, due to their simple structure and robustness to circuit imperfections. Exact analyses of the system are complicated by the presence of a discontinuous nonlinear element—a one-bit quantizer. In this paper, we study the dynamics of the one-dimensional mapping which models the behavior of the single-loop modulator. This mapping has a discontinuity at the origin and constant slope at all other points. With slope one, the dynamics in the region of interest reduce to those of the rotation of the circle. With slope less than one, almost all system inputs give rise to globally asymptotically stable periodic orbits. We emphasize the case with slope greater than one, and explain the structure of the resultant bifurcation diagram. A symbolic dynamics based study allows us to explain the self-similarity of the dynamics and the nature of chaos in the system.

scholarly journals Anchor Generation Optimization and Region of Interest Assignment for Vehicle Detection

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1089 ◽  
Author(s):  
Ye Wang ◽  
Zhenyi Liu ◽  
Weiwen Deng
Keyword(s):  
Pedestrian Detection ◽  
Region Of Interest ◽  
Vehicle Detection ◽  
Detection Accuracy ◽  
Fixed Size ◽  
Feature Maps ◽  
Feature Map ◽  
Bounding Box ◽  
New Feature ◽  
And Training

Region proposal network (RPN) based object detection, such as Faster Regions with CNN (Faster R-CNN), has gained considerable attention due to its high accuracy and fast speed. However, it has room for improvements when used in special application situations, such as the on-board vehicle detection. Original RPN locates multiscale anchors uniformly on each pixel of the last feature map and classifies whether an anchor is part of the foreground or background with one pixel in the last feature map. The receptive field of each pixel in the last feature map is fixed in the original faster R-CNN and does not coincide with the anchor size. Hence, only a certain part can be seen for large vehicles and too much useless information is contained in the feature for small vehicles. This reduces detection accuracy. Furthermore, the perspective projection results in the vehicle bounding box size becoming related to the bounding box position, thereby reducing the effectiveness and accuracy of the uniform anchor generation method. This reduces both detection accuracy and computing speed. After the region proposal stage, many regions of interest (ROI) are generated. The ROI pooling layer projects an ROI to the last feature map and forms a new feature map with a fixed size for final classification and box regression. The number of feature map pixels in the projected region can also influence the detection performance but this is not accurately controlled in former works. In this paper, the original faster R-CNN is optimized, especially for the on-board vehicle detection. This paper tries to solve these above-mentioned problems. The proposed method is tested on the KITTI dataset and the result shows a significant improvement without too many tricky parameter adjustments and training skills. The proposed method can also be used on other objects with obvious foreshortening effects, such as on-board pedestrian detection. The basic idea of the proposed method does not rely on concrete implementation and thus, most deep learning based object detectors with multiscale feature maps can be optimized with it.

Melanoma Image Classification Based on Multivariate Parametric Statistical Tests of Hypothesis

2019 ◽  
pp. 89-109
Author(s):  
K. Seetharaman
Keyword(s):  
Skin Lesion ◽  
Statistical Tests ◽  
Normal Skin ◽  
Region Of Interest ◽  
Feature Space ◽  
Advanced Stage ◽  
Initial Stage ◽  
Advanced Stages ◽  
Novel Method ◽  
Tests Of Hypotheses

This chapter proposes a novel method, based on the multivariate parametric statistical tests of hypotheses, which classifies the normal skin lesion images and the various stages of the melanoma images. The melanoma images are categorized into two classes, such as initial stage and advanced stage, based on the degree of aggressiveness of the cancer. The region of interest is identified and segmented from the input skin melanoma image. The features, such as HSV color, shape, and texture, are extracted from the region of interest. The features are treated as a feature space, which is assumed to be a multivariate normal random field. The proposed statistical tests are employed to identify and classify the melanoma images. The proposed method yields an average correct classification up to 91.55% for the normal skin lesion versus the initial and the advanced stages of the melanoma images, up to 91.39% for initial stage melanoma versus the normal skin lesion and the advanced stages melanoma, and up to 92.27% for the advanced stage melanoma versus the normal skin lesion and the initial stage melanoma. The proposed method yields better results.

scholarly journals Correlation between dermoscopic and histopathological diagnoses of atypical nevi in a dermatology outpatient clinic of the Medical School of São José do Rio Preto, SP, Brazil

2013 ◽  
Vol 88 (2) ◽  
pp. 199-203 ◽  
Author(s):  
João Roberto Antonio ◽  
Rosa Maria Cordeiro Soubhia ◽  
Solange Corrêa Garcia Pires D'Avila ◽  
Adriana Cristina Caldas ◽  
Lívia Arroyo Trídico ◽  
...  
Keyword(s):  
Outpatient Clinic ◽  
Skin Lesions ◽  
Histopathological Diagnosis ◽  
Melanocytic Nevi ◽  
Pigmented Lesions ◽  
Highly Sensitive ◽  
Advanced Stages ◽  
Malignant Lesions ◽  
Atypical Nevi

BACKGROUND: The incidence of cutaneous melanoma is increasing worldwide. Since it is an aggressive neoplasm, it is difficult to treat in advanced stages; early diagnosis is important to heal the patient. Melanocytic nevi are benign pigmented skin lesions while atypical nevi are associated with the risk of developing melanoma because they have a different histological pattern than common nevi. Thus, the clinical diagnosis of pigmented lesions is of great importance to differentiate benign, atypical and malignant lesions. Dermoscopy appeared as an auxiliary test in vivo, playing an important role in the diagnosis of pigmented lesions, because it allows the visualization of structures located below the stratum corneum. It shows a new morphological dimension of these lesions to the dermatologist and allows greater diagnostic accuracy. However, histopathology is considered the gold standard for the diagnosis. OBJECTIVES: To establish the sensitivity and specificity of dermoscopy in the diagnosis of pigmented lesions suspected of malignancy (atypical nevi), comparing both the dermatoscopic with the histopathological diagnosis, at the Dermatology Service of the outpatient clinic of Hospital de Base, São José do Rio Preto, SP. METHODS: Analysis of melanocytic nevi by dermoscopy and subsequent biopsy on suspicion of atypia or if the patient so desires, for subsequent histopathological diagnosis. RESULTS: Sensitivity: 93%. Specificity: 42%. CONCLUSIONS: Dermoscopy is a highly sensitive method for the diagnosis of atypical melanocytic nevi. Despite the low specificity with many false positive diagnoses, the method is effective for scanning lesions with suspected features of malignancy.

COST-SENSITIVE MULTI-VIEW LEARNING MACHINE

2014 ◽  
Vol 28 (03) ◽  
pp. 1451004
Author(s):  
ZHE WANG ◽  
MINGZHE LU ◽  
ZENGXIN NIU ◽  
XIANGYANG XUE ◽  
DAQI GAO
Keyword(s):  
Acquisition Cost ◽  
Original Data ◽  
Classification Performance ◽  
Data Sets ◽  
Learning Framework ◽  
Real World Application ◽  
Multiple Kernel ◽  
Learning Machine ◽  
Almost All ◽  
The Cost

Multi-view learning aims to effectively learn from data represented by multiple independent sets of attributes, where each set is taken as one view of the original data. In real-world application, each view should be acquired in unequal cost. Taking web-page classification for example, it is cheaper to get the words on itself (view one) than to get the words contained in anchor texts of inbound hyper-links (view two). However, almost all the existing multi-view learning does not consider the cost of acquiring the views or the cost of evaluating them. In this paper, we support that different views should adopt different representations and lead to different acquisition cost. Thus we develop a new view-dependent cost different from the existing both class-dependent cost and example-dependent cost. To this end, we generalize the framework of multi-view learning with the cost-sensitive technique and further propose a Cost-sensitive Multi-View Learning Machine named CMVLM for short. In implementation, we take into account and measure both the acquisition cost and the discriminant scatter of each view. Then through eliminating the useless views with a predefined threshold, we use the reserved views to train the final classifier. The experimental results on a broad range of data sets including the benchmark UCI, image, and bioinformatics data sets validate that the proposed algorithm can effectively reduce the total cost and have a competitive even better classification performance. The contributions of this paper are that: (1) first proposing a view-dependent cost; (2) establishing a cost-sensitive multi-view learning framework; (3) developing a wrapper technique that is universal to most multiple kernel based classifier.

scholarly journals Hierarchical Mergence Approach to Cell Detection in Phase Contrast Microscopy Images

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Lei Chen ◽  
Jianhua Zhang ◽  
Shengyong Chen ◽  
Yao Lin ◽  
Chunyan Yao ◽  
...  
Keyword(s):  
Phase Contrast ◽  
Region Of Interest ◽  
Cell Detection ◽  
Morphological Operations ◽  
Segmentation Methods ◽  
Otsu Method ◽  
Reference Images ◽  
Microscopy Images ◽  
Different Levels

Phase contrast microscope is one of the most universally used instruments to observe long-term cell movements in different solutions. Most of classic segmentation methods consider a homogeneous patch as an object, while the recorded cell images have rich details and a lot of small inhomogeneous patches, as well as some artifacts, which can impede the applications. To tackle these challenges, this paper presents a hierarchical mergence approach (HMA) to extract homogeneous patches out and heuristically add them up. Initially, the maximum region of interest (ROI), in which only cell events exist, is drawn by using gradient information as a mask. Then, different levels of blurring based on kernel or grayscale morphological operations are applied to the whole image to produce reference images. Next, each of unconnected regions in the mask is applied with Otsu method independently according to different reference images. Consequently, the segmentation result is generated by the combination of usable patches in all informative layers. The proposed approach is more than simply a fusion of the basic segmentation methods, but a well-organized strategy that integrates these basic methods. Experiments demonstrate that the proposed method outperforms previous methods within our datasets.

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