MRI mammogram image classification using ID3 algorithm

Angayarkanni, S. Pitchumani; Kamal, Nadira Banu

doi:10.1049/cp.2012.0464

Cited by 7 publications

(3 citation statements)

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“…The research conducted by ( Angayarkanni & Kamal, 2012 ) used MRI mammogram image dataset to test the performance and capability of the ID3 algorithm in the classification of breast cancer domain. The particular dataset consists of three class attributes, which are benign, malignant and normal.…”

Section: Methodsmentioning

confidence: 99%

Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition

Idris¹,

Ismail²

2021

PeerJ Computer Science

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Breast cancer becomes the second major cause of death among women cancer patients worldwide. Based on research conducted in 2019, there are approximately 250,000 women across the United States diagnosed with invasive breast cancer each year. The prevention of breast cancer remains a challenge in the current world as the growth of breast cancer cells is a multistep process that involves multiple cell types. Early diagnosis and detection of breast cancer are among the greatest approaches to preventing cancer from spreading and increasing the survival rate. For more accurate and fast detection of breast cancer disease, automatic diagnostic methods are applied to conduct the breast cancer diagnosis. This paper proposed the fuzzy-ID3 (FID3) algorithm, a fuzzy decision tree as the classification method in breast cancer detection. This study aims to resolve the limitation of an existing method, ID3 algorithm that unable to classify the continuous-valued data and increase the classification accuracy of the decision tree. FID3 algorithm combined the fuzzy system and decision tree techniques with ID3 algorithm as the decision tree learning. FUZZYDBD method, an automatic fuzzy database definition method, would be used to design the fuzzy database for fuzzification of data in the FID3 algorithm. It was used to generate a predefined fuzzy database before the generation of the fuzzy rule base. The fuzzified dataset was applied in FID3 algorithm, which is the fuzzy version of the ID3 algorithm. The inference system of FID3 algorithm is simple with direct extraction of rules from generated tree to determine the classes for the new input instances. This study also analysed the results using three breast cancer datasets: WBCD (Original), WDBC (Diagnostic) and Coimbra. Furthermore, the comparison of FID3 algorithm with the existing methods is conducted to verify the proposed method’s capability and performance. This study identified that the combination of FID3 algorithm with FUZZYDBD method is reliable, robust and managed to perform well in breast cancer classification.

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Section: Methodsmentioning

confidence: 99%

Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition

Idris¹,

Ismail²

2021

PeerJ Computer Science

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“…Angayarkanni and Kamal [87] evaluated the ID3 algorithm's performance and capability for breast cancer domain classification using an magnetic resonance imaging (MRI) mammogram image dataset. The aforementioned dataset has three distinct classes: benign, malignant, and normal.…”

Section: Decision Tree: Id3 Algorithmmentioning

confidence: 99%

Breast cancer disease classification using fuzzy-ID3 algorithm based on association function

Idris

Ismail

Mohamad

et al. 2022

IJ-AI

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Breast cancer is the second leading cause of mortality among female cancer patients worldwide. Early detection of breast cancer is considerd as one of the most effective ways to prevent the disease from spreading and enable human can make correct decision on the next process. Automatic diagnostic methods were frequently used to conduct breast cancer diagnoses in order to increase the accuracy and speed of detection. The fuzzy-ID3 algorithm with association function implementation (FID3-AF) is proposed as a classification technique for breast cancer detection. The FID3-AF algorithm is a hybridisation of the fuzzy system, the iterative dichotomizer 3 (ID3) algorithm, and the association function. The fuzzy-neural dynamicbottleneck-detection (FUZZYDBD) is considered as an automatic fuzzy database definition method, would aid in the development of the fuzzy database for the data fuzzification process in FID3-AF. The FID3-AF overcame ID3’s issue of being unable to handle continuous data. The association function is implemented to minimise overfitting and enhance generalisation ability. The results indicated that FID3-AF is robust in breast cancer classification. A thorough comparison of FID3-AF to numerous existing methods was conducted to validate the proposed method’s competency. This study established that the FID3-AF performed well and outperform other methods in breast cancer classification.

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“…A set of Mammogram images has been classified by Angayarkanni et al [8], and they achieved 99.50% accuracy using the Gray-Level-Cooccurence Matrix (GLCM) as feature. Gatuha et al [9] utilized Mammogram images for image classification using a total of 11 features and achieved 97.30% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

Nahid

Kong

2018

Information

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Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD) techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist's decision. Among the different image analysis techniques, classification of the images has always been a challenging task. Due to the intense complexity of biomedical images, it is always very challenging to provide a reliable decision about an image. The state-of-the-art Convolutional Neural Network (CNN) technique has had great success in natural image classification. Utilizing advanced engineering techniques along with the CNN, in this paper, we have classified a set of Histopathological Breast-Cancer (BC) images utilizing a state-of-the-art CNN model containing a residual block. Conventional CNN operation takes raw images as input and extracts the global features; however, the object oriented local features also contain significant information-for example, the Local Binary Pattern (

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MRI mammogram image classification using ID3 algorithm

Cited by 7 publications

References 0 publications

Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition

Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition

Breast cancer disease classification using fuzzy-ID3 algorithm based on association function

Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

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