Sumaira Tabassum ^* Muhammad Jawad Khan Javaid Iqbal Asim Waris Muhammad Adeel Ijaz

• National University of Sciences and Technology (NUST), Islamabad, Pakistan

Anomalous chromosomes are the cause of genetic diseases such as cancer, Alzheimer's, Parkinson's, epilepsy, and autism. Karyotype analysis is a standard procedure for diagnosing these genetic disorders. Identifying anomalies is often costly, time-consuming, and heavily reliant on expert interpretation and considerable manual efforts. Efforts are being made to automate karyogram analysis. However, the unavailability of large datasets, especially those including samples with chromosomal abnormalities, presents a significant challenge. The development of automated models requires extensive labeled and incredibly abnormal data to accurately identify and analyze abnormalities, which is difficult to obtain in sufficient quantities. Although the deep learning-based architecture has yielded state-of-the-art performance in medical image anomaly detection, it cannot generalize well due to the lack of anomalous datasets. This study introduces a novel hybrid approach that combines unsupervised and supervised learning techniques to overcome the challenges of limited labeled data and scalability in chromosomal analysis. An Autoencoder-based system is initially trained with unlabeled data to identify chromosome patterns.It is fine-tuned on labeled data, followed by a classification step using a Convolutional Neural Network (CNN). A unique dataset of 234,259 chromosome images, including training, validation, and test sets, is used. Marking a significant achievement in the scale of chromosomal analysis. The proposed hybrid system accurately detects structural anomalies in individual chromosome images, achieving an 99.3% accuracy in classifying chromosomes. We also used structural similarity index measure and template matching to identify the part of the abnormal chromosome that differs from the normal one. This automated model has the potential to significantly contribute to the early detection and diagnosis of chromosome related disorders that impact both genetic health and neurological behavior.