Frequency-domain information along with LSTM and GRU methods for histopathological breast-image classification
| Autor: | Abdullah-Al Nahid, Yinan Kong, Mohamad Ali Mehrabi |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
010504 meteorology & atmospheric sciences
Artificial neural network Contextual image classification Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Biomedical image Pattern recognition 010501 environmental sciences 01 natural sciences Data modeling Image (mathematics) ComputingMethodologies_PATTERNRECOGNITION Frequency domain Medical imaging Discrete cosine transform Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | ISSPIT |
| Popis: | Biomedical image classification has always been a challenging and critical task which has the highest level of importance. The Deep Neural Network (DNN) has been recently introduced for normal image classification and lately introduced for Biomedical image classification with some advanced engineering. In this paper we have classified an image dataset with a DNN utilizing Long Short Term Memory (LSTM) as well as Gated Recurrent Unit (GRU) for breast-image classification. Instead of directly using raw images, we have utilized frequency-domain information for the image classification. Using our model we have obtained 93.01% Accuracy, 94.00% Recall and 94.00% Precision, which is the best available result on this dataset. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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