Intelligent Chromosome Karyotyping: A Cutting-Edge Automated System for Accurate Detection and Analysis

Research and diagnostic medical activities heavily rely on chromosome classification methods to discover chromosomal abnormalities. The research investigates MobileNet and VGG network applications on chromosome categorization through analysis of the BioImLAB Chromosome Data Set for Classification. The dataset contains labeled chromosome images that need sophisticated feature extraction methods to achieve precise classification results. The presented work is pre-trained in MobileNet and VGG16 models using transfer learning techniques. The dataset complies with preprocessing steps which include image resizing together with normalization and data augmentation to enhance the model's generalization capabilities. An analysis is conducted to evaluate the training procedure via categorical cross-entropy loss with Adam optimization while utilizing multiple classification metrics to confirm performance. This research evaluates the effectiveness between MobileNet and VGG networks within chromosome classification. The research generates data that leads to developing strong deep learning models for cytogenetic applications which can automate karyotyping and genetic diagnostic processes.