Invasive Ductal Carcinoma Detection by A Gated Recurrent Unit Network with Self Attention

Biswas, Ananna; Nazi, Zabir Al; Abir, Tasnim Azad

doi:10.1109/eict48899.2019.9068841

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Cited by 2 publications

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Design and Build a Passenger Prediction Information System Using Gated Recurrent Unit Deep Learning Algorithm

Umam,

Dafid,

Adiputra

et al. 2023

2023 IEEE 9th Information Technology International Seminar (ITIS)

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Design and Build a Passenger Prediction Information System Using Gated Recurrent Unit Deep Learning Algorithm

Umam,

Dafid,

Adiputra

et al. 2023

2023 IEEE 9th Information Technology International Seminar (ITIS)

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Classification of Invasive Ductal Carcinoma from histopathology breast cancer images using Stacked Generalized Ensemble

Kumar

Batra

2021

IFS

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Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. However, Machine learning algorithms have given rise to progress across different domains. There are various diagnostic methods available for cancer detection. However, cancer detection through histopathological images is considered to be more accurate. In this research, we have proposed the Stacked Generalized Ensemble (SGE) approach for breast cancer classification into Invasive Ductal Carcinoma+ and Invasive Ductal Carcinoma-. SGE is inspired by the stacking model which utilizes output predictions. Here, SGE uses six deep learning models as level-0 learner models or sub-models and Logistic regression is used as Level – 1 learner or meta – learner model. Invasive Ductal Carcinoma dataset for histopathology images is used for experimentation. The results of the proposed methodology have been compared and analyzed with existing machine learning and deep learning methods. The results demonstrate that the proposed methodology performed exponentially good in image classification in terms of accuracy, precision, recall, and F1 measure.

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Invasive Ductal Carcinoma Detection by A Gated Recurrent Unit Network with Self Attention

Cited by 2 publications

References 12 publications

Design and Build a Passenger Prediction Information System Using Gated Recurrent Unit Deep Learning Algorithm

Design and Build a Passenger Prediction Information System Using Gated Recurrent Unit Deep Learning Algorithm

Classification of Invasive Ductal Carcinoma from histopathology breast cancer images using Stacked Generalized Ensemble

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