Cancer cell detection and prediction using image processing with Deep learning

Early cancer treatment and diagnosis an important aspects of detecting and predicting cancer cells. The application of deep learning algorithms and image processing methods can greatly improve the quality and speed of cell detection in medical images of cancer. Nonetheless, the existing technologies tend to be poor in such aspects as low accuracy, high false-positive rates, and inability to process extensive collections of images. Current approaches mainly use traditional image processing and Deep learning algorithms, which tend not to be able to generalize to different types of cancer cells, and have a problem with noisy medical images. Also, the scholar practice of detection seems to be slow, and the conventional algorithms might fail to identify complex structures in images. As a result, the proposed framework, Cancer Cell Detection using Deep Learning (CCD-DL), is a multi-stage image processing framework, feature extraction and classification. The CCD-ML model uses the methods of deep learning, namely, an Artificial Neural Network (ANN) in the classification stage. The ANN model is trained with a set of cancer cell images, and therefore, it can acquire sophisticated patterns and make the most accurate predictions. This solution would address such issues as overfitting, model inefficiency, and weak generalization. The suggested solution will help to make the process of cancer cell detection more efficient and accurate, which will make it more useful in clinical settings. The framework yields faster and more accurate diagnoses by improving the detection accuracy and reducing errors. The results of the implementation of this approach are promising, as the accuracy of classification is high and the error rates are lower in comparison to the current methods. This puts the CCD-DL framework as a powerful tool in combating cancer, giving clinicians an effective tool in the fight against the early onset and intervention.