A Fusion-Based Approach to Generate and Classify Synthetic Cancer Cell Image Using DCGAN and CNN Architecture

Abstract

The most talked about disease of our era, cancer, has taken many lives, and most of them are due to late prognosis. Statistical data shows around 10 million people lose their lives per year due to cancer globally. With every passing year, the malignant cancer cells are evolving at a rapid pace. The cancer cells are mutating with time, and it's becoming much more dangerous than before. In the chapter, the authors propose a DCGAN-based neural net architecture that will generate synthetic blood cancer cell images from fed data. The images, which will be generated, don't exist but can be formed in the near future due to constant mutation of the virus. Afterwards, the synthetic image is passes through a CNN net architecture which will predict the output class of the synthetic image. The novelty in this chapter is that it will generate some cancer cell images that can be generated after mutation, and it will predict the class of the image, whether it's malignant or benign through the proposed CNN architecture.

A lightweight hybrid model for the automatic recognition of uterine fibroid ultrasound images based on deep learning

“…DCGAN enhances data by incorporating convolution operations into GAN, creating more realistic images that excel in data augmentation applications. [21][22][23] The paper suggests combining the real-time classification capability of MobileNetV2 with the high-quality data generation ability of DCGAN to create a lightweight hybrid model. This model is designed for the real-time detection and classification of uterine fibroids during surgery, aiming to aid doctors in diagnosis.…”

“…Deep learning can produce higher‐quality images than traditional methods like random rotation, cropping, and flipping. DCGAN enhances data by incorporating convolution operations into GAN, creating more realistic images that excel in data augmentation applications 21–23 . The paper suggests combining the real‐time classification capability of MobileNetV2 with the high‐quality data generation ability of DCGAN to create a lightweight hybrid model.…”