Detection of breast cancer from histopathology image and classifying benign and malignant state using fuzzy logic

Johra, Fatema-Tuz; Shuvo, Md. Maruf Hossain

doi:10.1109/ceeict.2016.7873137

Cited by 27 publications

(4 citation statements)

References 11 publications

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“…According to [68], the adoption of the fuzzy approach is effective at detecting breast cancer, with an accuracy of greater than 98%. Furthermore, according to [69], when used to a histopathology image dataset, the fuzzy model attained an accuracy of 94.26%. Meanwhile, Schaefer et al [70] shown that when applied a fuzzy technique, the experimental findings of breast cancer thermogram classification reached an accuracy of 80%.…”

Section: Methods 21 Fuzzy Systemmentioning

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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Section: Methods 21 Fuzzy Systemmentioning

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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show abstract

“…The study performed by Ali & Mutlag (2018) conveys that the fuzzy method’s implementation is useful in detecting breast cancer as the accuracy reaches over 98%. The study made by Johra & Shuvo (2017) shows that the fuzzy logic model’s accuracy is 94.26% when applied with histopathology image dataset to classify benign and malignant cells in breast cancer tumours. Result of breast cancer thermogram classification by Schaefer et al (2007) that also implemented the fuzzy method had achieved the diagnostic accuracy rate of 80%.…”

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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“…IDC originates in a milk duct and then spreads to the breast's fibrous or fatty tissue. It may spread to lymph nodes and other body locations [6]. Primary detection and accurate diagnosis can minimize breast cancer mortality rates.…”

Section: Introductionmentioning

confidence: 99%

Breast invasive ductal carcinoma diagnosis using machine learning models and Gabor filter method of histology images

Kadhim

Kamil²

2023

IJRES

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Breast cancer is the most common type of cancer in women and the leading cause of death from a malignant growth in the world. Machine learning methods have been created to help with cancer detection accuracy. There are several methods for detecting cancer. Histopathological images are more accurate. In this study, we employed the Gabor filter to extract statistical features from invasive ductal carcinoma histopathology images. From the histopathological images, we chose 100, 200, 400, 1000, and 2000 at random. These statistical features were used to train several models to classify these images as malignant or benign, including the decision tree, quadratic discriminant analysis, extra randomized trees, gradient boosting, Gaussian process, Naive Bayes, nearest centroid, multilayer perceptron, and support vector machine. The models' accuracy, sensitivity, specificity, precision, and F1_score were examined. The models produced the highest results when there were 100 images and a wavenumber of 0.2. While as the number of images increased, the models' effectiveness reduced. The most obvious finding to emerge from this study is that we suggest using deep learning instead of machine learning models for large datasets.

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Detection of breast cancer from histopathology image and classifying benign and malignant state using fuzzy logic

Cited by 27 publications

References 11 publications

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

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

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

Breast invasive ductal carcinoma diagnosis using machine learning models and Gabor filter method of histology images

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