Breast Cancer Classification and Prediction using Machine Learning

Sunny, Jean; Rane, Nikita D.; Kanade, Rucha; Devi, Sulochana

doi:10.17577/ijertv9is020280

Cited by 24 publications

(9 citation statements)

References 1 publication

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“…In CNN, the image had a 62% correct classification rate and a 36% incorrect classification rate. The classification rate in DenseNet [31] is75% for correctly classified items and 20.85% for incorrectly classified items. The efficacy of the suggested method can be compared to several cutting-edge approaches that are employed for the classification of BC on histopathology images.…”

Section: Idc Dataset Analysismentioning

confidence: 99%

Classification of Breast Cancer using Ensemble Filter Feature Selection with Triplet Attention Based Efficient Net Classifier

Bangalore Nagaraj,

Shivanandamurthy Hiremath,

Matta Polnaya

2024

IAJIT

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In medical imaging, the effective detection and classification of Breast Cancer (BC) is a current research important task because of the still existing difficulty to distinguish abnormalities from normal breast tissues due to their subtle appearance and ambiguous margins and distinguish abnormalities from the normal breast. Moreover, BC detection based on an automated detection model is needed, because manual diagnosis faces problems due to cost and shortage of skilled manpower, and also takes a very long time. Using deep learning and ensemble feature selection techniques, in this paper, a novel framework is introduced for classifying BC from histopathology images. The five primary steps of the suggested framework are as follows: 1) to make the largest original dataset and then deep learning model with data augmentation to improve the learning. 2) The best features are selected by an Ensemble Filter Feature selection Method (EFFM) which combines the best feature subsets to produce the final feature subsets. 3) Then the pruned Convolution Neural Network (CNN) model is utilized to extract the optimal features. 4) Finally, the classification is done through the Triplet Attention based Efficient Network (TAENet) classifier. The suggested model produces a 98% accuracy rate after being trained and tested on two different histopathology imaging datasets including images from four different data cohorts. Subsequently, the suggested strategy outperforms the conventional ones since the ensemble filter habitually acquires the best features, and experimental results demonstrate the importance of the proposed approach

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Section: Idc Dataset Analysismentioning

confidence: 99%

Classification of Breast Cancer using Ensemble Filter Feature Selection with Triplet Attention Based Efficient Net Classifier

Bangalore Nagaraj,

Shivanandamurthy Hiremath,

Matta Polnaya

2024

IAJIT

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“…14) Nikitha Rane, Jean Sunny presented work on the classification of Cancer using machine learning concepts and their major discussion point is detecting cancer in very early stages so that a lot of lives can be saved [14].…”

Section: IImentioning

confidence: 99%

Multiple Disease Detection System Using Machine Learning

Gulzar

2024

IJRASET

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In recent times, there has been a surge of interest in employing machine learning techniques for the early and accurate detection of various diseases. This research introduces a holistic approach to constructing a robust multi-disease detection system that utilizes advanced machine learning algorithms. Our proposed system integrates diverse datasets encompassing a range of medical conditions, enabling the simultaneous detection of multiple diseases within a unified framework. We leverage cutting-edge machine learning models, including, but not limited to, [specify the models used], to analyze and interpret intricate patterns within the data.

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“…The importance of the tree correlation is increased by the random forest's superior accuracy performance [30]. The risk of overfitting is decreased, and the error and converge into some value is generalized, by building a lot of trees into a random forest [20,31].…”

Section: 𝐺𝑎𝑖𝑛(𝑡 𝑥) = 𝐸(𝑡) − 𝐸(𝑡 𝑥)mentioning

confidence: 99%

Breast Cancer Detection Using Comprising Fuzzy C-Means and Artificial Bee Colony Optimization Segmentation and Grading with Random Forest Classifier

Sundaravadivelu,

Santhanakrishnan

2023

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The integration of machine learning models into artificial intelligence has precipitated significant advancements in medical science. Notably, various programs have equipped radiologists with valuable tools to aid in medical image processing. Breast cancer stands out as the most prevalent cancer among women globally. The automated detection and classification of lesions in mammograms remain critical challenges necessitating more accurate diagnosis and meticulous examination of concerning lesions. Mammography is a pivotal diagnostic procedure for early breast cancer detection, enabling individuals to identify changes in their breasts far before they are palpable. In the relentless quest to improve patient care and tackle the prevalent ailments of our time, diverse fields such as data mining and artificial intelligence are making substantial contributions to breast cancer analysis. A groundbreaking investigation is currently focused on developing an innovative image processing technique aimed at detecting and grading breast cancer using mammogram and MRI images. This research relies on a unique image segmentation method utilizing a newly devised algorithm, coined CABC (Comprising Fuzzy C-Means and Artificial Bee Colony optimization). This inventive algorithm synergistically combines the benefits of FCM (Fuzzy C-Means) clustering and the robustness of Artificial Bee Colony (ABC) optimization. To determine the cancer stage, a random forest classifier is used, thereby enhancing the precision of the evaluation. The results stemming from the application of the CABC algorithm have demonstrated an impressive accuracy rate of 89.17%, attesting to the effectiveness of the proposed methodology. To thoroughly assess its performance, a comparative analysis has been conducted, involving other methodologies such as k-Means, Context-Based Clustering, Random Forest, and FCM individually. This rigorous evaluation employs both confusion matrix parameters and decision parameters, conclusively validating the superior performance of the proposed method. Essentially, this study exemplifies the synergistic collaboration between sophisticated image processing techniques, advanced clustering algorithms, and machine learning classifiers in refining breast cancer detection and grading. The empirical evidence presented highlights the potential of the CABC algorithm as a trailblazing instrument in this essential area of medical research.

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Breast Cancer Classification and Prediction using Machine Learning

Cited by 24 publications

References 1 publication

Classification of Breast Cancer using Ensemble Filter Feature Selection with Triplet Attention Based Efficient Net Classifier

Classification of Breast Cancer using Ensemble Filter Feature Selection with Triplet Attention Based Efficient Net Classifier

Multiple Disease Detection System Using Machine Learning

Breast Cancer Detection Using Comprising Fuzzy C-Means and Artificial Bee Colony Optimization Segmentation and Grading with Random Forest Classifier

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