An effective image denoising using PPCA and classification of CT images using artificial neural networks

Mredhula, L.; Dorairangaswamy, M. A.

doi:10.1504/ijmei.2017.10000840

Cited by 2 publications

(3 citation statements)

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“…Pixel surge model (PSM) and "Probabilistic Principal Component Analysis" (PPCA) are used to improve the denoised image output with morphological operations, filtering, and region props methods. [100] In the second phase, the neural networkbased classifier is used for classification and shos the best performance with an accuracy of 88%. [100] Devi et al experiment with the classification model using an artificial neural network that produces good results.…”

Section: Classification Performancementioning

confidence: 99%

“…[100] In the second phase, the neural networkbased classifier is used for classification and shos the best performance with an accuracy of 88%. [100] Devi et al experiment with the classification model using an artificial neural network that produces good results. Authors perform a survey on different types of architecture such as Multi-Layer perceptron (MLP), cascaded and hybrid MLP, CNN, RBF network, feed-forward network, knowledgebased network.…”

Section: Classification Performancementioning

confidence: 99%

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Automated Detection and Classification of Cervical Cancer Using Pap Smear Microscopic Images: A Comprehensive Review and Future Perspectives

Shanthi¹,

Hareesha²,

Kudva³

2022

Eng. Sci.

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Computer aided categorization of smear images has been considered challenging in the past few decades. Cervical cancer is a main cause of mortality among women worldwide and more prevalent in underdeveloped countries. This disease can be successfully treated, even fully cured, if detected at its early phase. Computerized image analysis methods are primarily of great interest as they provide significant benefits for clinicians with reliable and timely diagnosis of the samples. Dedicated image analysis algorithms provide mathematical description of the region of interest which provide a great support to pathologists for decision making. In this review, we have outlined state of the art techniques expressed in prominent publications on computer assisted diagnostic system for cancer detection. By utilizing the domain aspects of cervical cancer, suitable methods and techniques are explored and presented. This review also presents a knowledge to assess the methodology used in the literature and emphasized some of the inadequacies and weaknesses in the reviewed methods. The study accentuates the future directions pertinent to the development of cost-effective, automated disease classification system that should be a significant advantage for countries with limited resources and treatment services.

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Section: Classification Performancementioning

confidence: 99%

Section: Classification Performancementioning

confidence: 99%

Automated Detection and Classification of Cervical Cancer Using Pap Smear Microscopic Images: A Comprehensive Review and Future Perspectives

Shanthi¹,

Hareesha²,

Kudva³

2022

Eng. Sci.

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show abstract

“…The choice of an approach is very difficult (Yasodha et al, 2013). However, one can derive more reasonable properties that one seeks to obtain in a segmentation algorithm or classification (Mredhula and Dorairangaswamy, 2017), in particular:…”

Section: Problematicmentioning

confidence: 99%

Detection of abnormal blood cells by segmentation and classification

Bouzid-Daho

Boughazi

Petit

2019

IJMEI

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Leukaemia is a cancer of the hematopoietic cells. The detection of abnormal blood cells before cancer degeneration is a medical problem. The aim of our work is to obtain maximum recognition rate of leukaemia. We propose the development of a system based on mathematical morphology and k-means methods capable of segmentation, classification, and detection of the cancerous blood cells. This allows the characterisation and the description the cancerous region, which is an important task in the interpretation and diagnosis of pathologies present in blood. The segmentation was carried out using an efficient and fast algorithmic processing. It turns out that the proposed system shows to better segmentation and classification for tested images. The obtained experimental results are very encouraging which help hematologists for identification of abnormal blood cells.

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An effective image denoising using PPCA and classification of CT images using artificial neural networks

Cited by 2 publications

References 0 publications

Automated Detection and Classification of Cervical Cancer Using Pap Smear Microscopic Images: A Comprehensive Review and Future Perspectives

Automated Detection and Classification of Cervical Cancer Using Pap Smear Microscopic Images: A Comprehensive Review and Future Perspectives

Detection of abnormal blood cells by segmentation and classification

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