Machine- and deep-learning techniques have been used in numerous real-world applications. One of the famous deeplearning methodologies is the Deep Convolutional Neural Network. AlexNet is a well-known global deep convolutional
neural network architecture. AlexNet significantly contributes to solving different classification problems in different
applications based on deep learning. Therefore, it is necessary to continuously improve the model to enhance its
performance. This survey study formally defined the AlexNet architecture, presented information on current
improvement solutions, and reviewed applications based on AlexNet improvements. This work also presents a simple
survey based on a fusion of AlexNet with different machine-learning techniques for recent research in biomedical
applications. In the survey results for about 11 research papers for both improvement and fusion techniques of AlexNet,
it was clear that the fusion was the superior one with 99.72, and the improved one was 99.7%. In the conclusion and
discussion section, there was a comparison between the improved techniques and fusion techniques of AlexNet and a
proposal for future work on AlexNet development.