Advancing neuroblastoma diagnosis in paediatrics: Utilizing CNN-GRU and machine learning for improved detection and prediction

Neuroblastoma is the most common extracranial solid malignancy in children. It is possible to estimate the cancer's unpredictable biological activity based on the patient's age, genetic makeup, the biology of the cancer, and the extent of disease at diagnosis. Machine learning algorithms have the potential to enhance the precision and efficacy of cancer diagnosis, individualized therapy selection, and long-term outcome prediction. A subset of machine learning known as Artificial Intelligence (AI) is capable of spotting patterns in data and acting without the need for special programming to accomplish predetermined objectives. The patient populations most likely to benefit from advanced imaging tests may be enriched, high-risk populations can be identified, and individualized screening tests can be prescribed with the aid of machine learning technologies. Modern computational tools are becoming more and more crucial in pediatric oncology because of their invasive nature and the requirement for an early and precise diagnosis. Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) are two methods that can be used to increase the precision and predictability of detection. The Grey-Level Cooccurrence Matrix (GLCM) extracts the features as energy, entropy, dissimilarity, homogeneity, and contrast. The CNN-GRU to produce the results as precision, recall, accuracy, and F1-score 93%, 80%, 95%, and 83%.