Regularized Logistic Regression Model for Cancer Classification

Arafa, Ahmed; Radad, Marwa; Badawy, Mohammed; El-Fishawy, Nawal

doi:10.1109/nrsc52299.2021.9509831

Cited by 5 publications

(1 citation statement)

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“…Logistic regression was chosen as a combination of the VGG-19 because this algorithm has high flexibility, especially in the regularization selection, such as lasso and ridge [9]. Several studies have shown the effect of the lasso and ridge regularization in various Logistic regression classifications: in the COVID-19 patient classification , the lasso regularization performs better than the ridge [14]; in the breast cancer classification, the ridge regularization performs better than the lasso regularization [15]; and research C. This research uses the VGG-19 to transfer the feature extraction into the Logistic regression algorithm's dataset for classifying the feral cat images. We compare the accuracy, precision, and recall from each of the six models after combining lasso and ridge regularization with different cost parameter values.…”

Section: Imentioning

confidence: 99%

Inception-V3 Versus VGG-16: in Rice Classification Using Multilayer Perceptron

Firmansyah¹,

Rosnelly

Wanayumini³

2023

ICoSTEC

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Rice is an intriguing research topic, particularly in computer vision fields, because it is a staple food consumed in many parts of the world. Different rice varieties can be classified using the rice grain image based on their textures, sizes, and colors. To extract features from rice images, we used two popular pre trained convolutional neural network models, Inception V3 and VGG 16. The extracted features are then used as transfer learning in six variations of multilayer perceptron models, using rectified linear units as the activation function and adaptive moments as the loss function. The results show that the VGG 16 network performs better than the Inception V3, with 0.5% higher accuracy, precision, and recall value. Also, using the VGG 16 network produces a lower misclassification percentage, compared to the Inception V3 network, with a difference of 2.6%.

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Section: Imentioning

confidence: 99%