Predicting Breast Cancer Based on Optimized Deep Learning Approach

Saleh, Hager; ghany, Sara F. Abd-el; Alyami, Hashem; Alosaimi, Wael

doi:10.1155/2022/1820777

Cited by 35 publications

(19 citation statements)

References 26 publications

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“…The study on FNN has claimed accuracy of 99.3% over the limited-size dataset with 32 features. The RNN-based tumor prediction model 16 experimented over the Wisconsin dataset with an accuracy of 93.86%. But the RNN models have the limitations of vanishing gradients and complex training procedures.…”

Section: Literature Reviewmentioning

confidence: 99%

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

Praveen

Srinivasu

Shafi

et al. 2022

Sci Rep

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Carcinoma is a primary source of morbidity in women globally, with metastatic disease accounting for most deaths. Its early discovery and diagnosis may significantly increase the odds of survival. Breast cancer imaging is critical for early identification, clinical staging, management choices, and treatment planning. In the current study, the FastAI technology is used with the ResNet-32 model to precisely identify ductal carcinoma. ResNet-32 is having few layers comparted to majority of its counterparts with almost identical performance. FastAI offers a rapid approximation toward the outcome for deep learning models via GPU acceleration and a faster callback mechanism, which would result in faster execution of the model with lesser code and yield better precision in classifying the tissue slides. Residual Network (ResNet) is proven to handle the vanishing gradient and effective feature learning better. Integration of two computationally efficient technologies has yielded a precision accuracy with reasonable computational efforts. The proposed model has shown considerable efficiency in the evaluating parameters like sensitivity, specificity, accuracy, and F1 Score against the other dominantly used deep learning models. These insights have shown that the proposed approach might assist practitioners in analyzing Breast Cancer (BC) cases appropriately, perhaps saving future complications and death. Clinical and pathological analysis and predictive accuracy have been improved with digital image processing.

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Section: Literature Reviewmentioning

confidence: 99%

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

Praveen

Srinivasu

Shafi

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Another study [16] tried to examine the findings and to analyze several ML approaches for the detecting of BC using the same dataset. Another study [17] proposed an efficient recursive neural network (RNN) approach for BC classification using RNN and -Keras-Tuner‖ enhancement method in which they claimed that, the developed model achieved high performance accuracy. However, the study in [16] showed that Logistic regression classifier beats the other classifiers in predicting BC disease using BC Wisconsin (Diagnostic) data set (BCWD).…”

Section: Literature Reviewsmentioning

confidence: 99%

An Improved Breast Cancer Classification Method Using an Enhanced AdaBoost Classifier

Qawqzeh¹,

Alourani²,

Ghwanmeh³

2023

IJACSA

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The goal of this research is to create a machine learning (ML) classifier that can improve breast cancer (BC) diagnosis and prediction. The principle components analysis (PCA) technique is used in this work to minimize the dimensions of the BC dataset and achieve better classification metrics. The developed classifier outperformed others in terms of F1 score and accuracy score. Using the original BC dataset, four different classifiers are applied to determine the best classifier in terms of performance metrics. The used classifiers were RandomForest, DecisionTree, AdaBoost, and GradientBoosting. The RandomForest classifier obtained (95.7%) f1 score and (94.5%) accuracy score, the DecisionTree classifier obtained (93%) f1 score and (91%) accuracy score, the GradientBoosting classifier obtained (95%) f1 score and (93.5%) accuracy score, and the AdaBoost classifier obtained (95.8%) f1 score and (94.5%). The AdaBoost classifier was utilized to create the final model using the reduced PCA dataset because it scored the highest performance metrics. The developed classifier is named as "pcaAdaBoost". The optimized pcaAdaBoost achieved higher performance metrics in terms of f1 score (99%) and accuracy score (98.8%). The results reveal that the optimized pcaAdaBoost scored highest performance measures in terms of crossvalidation and testing outcomes, with an overall accuracy of (99%). The improved results justify the use of dimensionality reduction in high-dimension datasets to reduce complexity and improve performance measures.

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“…There are many techniques that have been introduced in the field of breast cancer prediction. Techniques such as deep learning [4], machine learning [5], and artificial intelligence [6]. Several ML techniques, including Support Vector Machine [7], Genetic [8], and classification techniques, are used to predict breast cancer.…”

Section: Introductionmentioning

confidence: 99%

A Review on Prediction Model for Breast Cancer Using Machine Learning

2023

IRJMETS

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One of the commonly encountered in which blood cells begin to proliferate erratically is breast cancer. There are several forms of breast cancer that can be fatal to the patient who's been diagnosed with it. Numerous things, such as genes, changes in the economy, social circumstances, and lifestyle choices, might contribute to it. Several research and articles have been written in this area to address the problem of such a terminal disease and offer answers by figuring out how to assist patients.In order to better comprehend the project's approach and to compare and contrast various machine learning algorithms, this paper is aimed at informing the reader about breast cancer. Machine learning can be employed to identify undetected trends for the identification of breast cancer by emphasizing the creation of a model with a high level of accuracy. The suggested strategy can be integrated with other machine learning approaches depending on the appropriateness and requirements of the project.

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Predicting Breast Cancer Based on Optimized Deep Learning Approach

Cited by 35 publications

References 26 publications

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

An Improved Breast Cancer Classification Method Using an Enhanced AdaBoost Classifier

A Review on Prediction Model for Breast Cancer Using Machine Learning

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