A survey on artificial intelligence approaches for medical image classification

Deepa, S. N.; Devi, B. Aruna

doi:10.17485/ijst/2011/v4i11.35

Cited by 101 publications

(45 citation statements)

References 64 publications

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“…For example, regions growing, spliting, and merging. There are many algorithms based on artificial intelligence [10], which are justified by the advantages that they offer. One advantage that they offer is the possibility to learn from the sample case provided by the artificial neural networks [11]or the deep neuronal network [12].…”

Section: Related Studiesmentioning

confidence: 99%

An improved segementation approach for skin lesion classification

Filali

Sabri

Aarab

2019

Stat., optim. inf. comput.

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Skin cancer is considered as one of the dangerous types of cancer with a high proportion of deaths. This cancer can be categorized into two main types; Melanoma, which is the deadliest form, and Non-Melanoma. Early melanoma detection and diagnosis allows more treatment options and decreases significantly the number of deaths. Many researchers proposed to use image processing for skin lesion detection. The process can be divided into three main stages: lesion identification based on image segmentation, features extraction and lesion classification. Segmentation and features extraction are the key-steps and significantly influence the outcome of the classification results. In this paper, an improved segmentation approach for skin lesion detection and classification has been proposed. The proposed approach consists on a pre-processing based on multiscale decomposition thats separate the input image into two components. The geometrical component will be used in the segmentation stage and the texture component in features extraction. The efficiency and the performance of the proposed approach has been evaluated in comparison with recent and robust dermoscopic approaches from literature.

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Section: Related Studiesmentioning

confidence: 99%

An improved segementation approach for skin lesion classification

Filali

Sabri

Aarab

2019

Stat., optim. inf. comput.

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“…In this model they are reducing the noise of the mean shifting clustering.in SVM to detect the tumor in the image.in brain tumor detection, precision is increased the noisy images. [5] [15] De-speckle the noise is done by removing the noise and obtaining the output image but obtaining mean, variance and wavelet selection is used for thresholding technique and segmenting the edges was improved using wavelets in this paper. [6] The classification of flower images from database and database is divided into 4 parts.…”

Section: Related Workmentioning

confidence: 99%

Detection of Leukemia using Image Processing

Mohammad*,

Sainath,

Babu

et al. 2019

IJITEE

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In olden days, cancer detection at early stage and its prevention became a difficult task to predict and enhance the stage and efficient image techniques were not available. Due to wrong analysis of cancer presence, patients are treated wrongly. In our method, we detect acute myeloid leukemia effectively. After plotting data from image histogram plot and knowing the segmented region, area of cancer can be obtained by classification of cancer and non-cancer cells. Wrong diagnosis leads to patient’s death, so adequate steps are taken to make diagnosis accurate and to know the features. Effective usage of image tools and regions of infected part are extracted, and detection of cancer based on respective growth of region is observed which is 91 accurate.

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“…A survey on medical image classification using artificial neural network approaches is presented in [20], where the applications of intelligent computing techniques for diagnostic sciences in biomedical image classification are addressed. Some artificial intelligence methods are also mentioned which are used commonly together to solve the special problems of medicine.…”

Section: Neural Classifiermentioning

confidence: 99%

Wavelet Based Histogram of Oriented Gradients Feature Descriptors for Classification of Partially Occluded Objects

Singh¹,

Shukla²,

Tiwari³

et al. 2015

IJISA

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Computer vision applications face various challenges while detection and classification of objects in real world like large variation in appearances, cluttered back ground, noise, occlusion, low illumination etc.. In this paper a Wavelet based Histogram of Oriented Gradients (WHOG) feature descriptors are proposed to represent shape information by storing local gradients in image. This results in enhanced representation of shape information. The performance of the feature descriptors are tested on multiclass image data set having partial occlusion, different scales and rotated object images. The performance of WHOG feature based object classification is compared with HOG feature based classification. The matching of test image with its learned class is performed using Back Propagation Neural Network (BPNN) algorithm. Proposed features not only performed superior than HOG but also beat wavelet, moment invariant and Curvelet.

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A survey on artificial intelligence approaches for medical image classification

Cited by 101 publications

References 64 publications

An improved segementation approach for skin lesion classification

An improved segementation approach for skin lesion classification

Detection of Leukemia using Image Processing

Wavelet Based Histogram of Oriented Gradients Feature Descriptors for Classification of Partially Occluded Objects

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