Bone Cancer Detection &amp; Classification Using Fuzzy Clustering &amp; Neuro Fuzzy Classifier

Hossain, Eftekhar; Rahaman, Mohammad Anisur

doi:10.1109/ceeict.2018.8628164

Cited by 14 publications

(17 citation statements)

References 10 publications

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“…However, it failed to consider more relevant features for increasing classification performance and also failed to predict in complex images. Hossain and Rahaman 27 proposed a method using fuzzy clustering and neurofuzzy classifier for detecting bone cancer. It uses an adaptive neurofuzzy inference system (ANFIS) for the classification of benign and malignant bone cancer.…”

Section: Literature Surveymentioning

confidence: 99%

Fractional‐Harris hawks optimization‐based generative adversarial network for osteosarcoma detection using Renyi entropy‐hybrid fusion

Badashah¹,

Basha

Ahamed

et al. 2021

Int J Intell Syst

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Osteosarcoma is the malignant bone sarcoma that is characterized by widespread genomic disruption and the inclination for metastatic spread. Early detection of osteosarcoma increases the survival rate. Various osteosarcoma detection methods are adopted to detect osteosarcoma at an early stage, but evaluating the slides under the microscope to find the degree of tumor necrosis and tumor result is a major challenge in the medical sector. Hence, an effective detection method is developed using the proposed Fractional-Harris Hawks Optimization-based Generative Adversarial Network (F-HHO-based GAN) for detecting osteosarcoma at an early stage. Here, the proposed F-HHO is designed by the integration of Fractional Calculus and HHO, respectively. Accordingly, the classification of viable tumor, nontumor, and the necrotic tumor is carried out by GAN using the histology image slides. GAN is used to perform osteosarcoma detection based on the features extracted from the image through the process of cell segmentation. The training process of GAN is done using the proposed F-HHO algorithm. However, the proposed F-HHO obtained better performance using the metrics, namely, accuracy, sensitivity, and specificity with the values of 98%, 98%, and 98% for training percentage and 96.282%, 97.552%, and 95.651% for K-fold, respectively.

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Section: Literature Surveymentioning

confidence: 99%

Fractional‐Harris hawks optimization‐based generative adversarial network for osteosarcoma detection using Renyi entropy‐hybrid fusion

Badashah¹,

Basha

Ahamed

et al. 2021

Int J Intell Syst

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show abstract

“…For brain tumor detection 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ANFIS achieved great results with an accuracy range of 96%-99%, and for Figure 1. The workflow of the proposed method bone cancer detection 15 the accuracy was 93%. Breast cancer detection 16 , 17 , 18 , 19 , the detection was never better and the ANFIS classifier achieved an accuracy of the range 91%-99% .…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is a mathematical method that considers the spatial pixel relationship in the image. This function computes how often a specific value of a pair of pixels occurring in an image (this characterizes the texture of the image) 15 , 16 . This computing occurs within 45 degree interval in four specific directions: 0, 45, 90 and 135 and scalar distance (number of neighbour pixels).…”

Section: Feature Extraction Stepmentioning

confidence: 99%

“…The extracted features are applied to the adaptive neuro fuzzy inference system. ANFIS classifier is a mixture of neural and fuzzy inference system networks 15 and its architecture is constructed by five layers as shown in figure 2. 38 , 36 The input values enters the first layer (The Fuzzy layer) which determines the membership functions belonging to each one, that's why it is called fuzzification layer.…”

Section: Anfis Classifier Structurementioning

confidence: 99%

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ANFIS-Net for Automatic Detection of COVID-19

Al-Ali

Elharrouss

Qidwai

et al. 2021

Preprint

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Among the most leading causes of mortality across the globe is infectious diseases. Human Immunodeficiency Virus infection and Acquired Immune Deficiency Syndrome (HIV/AIDS), H1N1 influenza and Poliomyelitis, Severe Acute Respiratory Syndrome (SARS) are all infectious diseases that has cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control, has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As Fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic detection of chest X-ray Images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. The results were promising, reaching 98.67% accuracy compared with the other state-of-the-art techniques.

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“…Guimarães et al [13] implemented a hybrid interpretive method insertion. Artificial neural networks synergy concepts forms base for this system.…”

Section: Related Workmentioning

confidence: 99%

A Critical Review on Medical Image Processing Techniques

2020

jcr

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Medical image processing is highly a vital task in the field of health informatics. Various techniques are used to process the images captured using methods like Medical Resonance Imaging (MRI), Computed Tomography(CT), Positron Emission Tomography (PET) and the process includes, denoising, feature extraction, segmentation and classification. Different methods are implemented for every process. Process of eliminating noise from an acquired image is termed as denoising and various filters are designed for the same. Different features like local binary pattern, gray level similarity are extracted for segmentation process. Clustering algorithms like fuzzy-c-means, fuzzy-k-means algorithms are used for image segmentation. Particle Swarm Optimization (PSO), convolutional neural networks are also used for the purpose of image segmentation. Performance of these methods are compared using various performance evaluation metrics like, precision, recall, accuracy, sensitivity.

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Bone Cancer Detection & Classification Using Fuzzy Clustering & Neuro Fuzzy Classifier

Cited by 14 publications

References 10 publications

Fractional‐Harris hawks optimization‐based generative adversarial network for osteosarcoma detection using Renyi entropy‐hybrid fusion

Fractional‐Harris hawks optimization‐based generative adversarial network for osteosarcoma detection using Renyi entropy‐hybrid fusion

ANFIS-Net for Automatic Detection of COVID-19

A Critical Review on Medical Image Processing Techniques

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