Transfer Learning with AlexNet and SqueezeNet for Enhanced Mammography Image Classification
Keywords:
Breast Cancer (BC), Deep Learning (DL), Machine Learning (ML),Convolutional Neural Network (CNN), Transfer Learning (TL), Adaptive Moment Estimation (ADAM), Stochastic Gradient Descent with Momentum (SGDM)Abstract
Breast cancer is a leading cause of death among women worldwide. Early detection and appropriate treatment can significantly reduce death rates. The ability of Deep Convolutional Neural Networks (DCNNs) to achieve state-of-the-art accuracy in image classification tasks has made them a popular choice for researchers in disease diagnosis. This paper aims to use two different DCNN architectures, AlexNet and SqueezeNet, to classify mammography images using a transfer learning approach. It also highlights the impact of preprocessing of images and optimizer selection (ADAM: Adaptive Moment Estimation, SGDM: Stochastic Gradient Descent with Momentum) on identifying intricate features from mammography images, thereby improving classification accuracy. From the achieved simulation results, the combination of AlexNet with ADAM was found to perform better, with a classification accuracy of 84%, compared to other combinations such as AlexNet+SGDM (80%), SqueezeNet+ADAM(80%), and SqueezeNet+SGDM (71%). Various machine learning performance measures were evaluated and compared with existing research targeting similar problems with DCNNs. Overall, the use of DCNNs with more optimal hyperparameter values shows promise for further improvement in classification accuracy.
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