Deep Learning for the Investigation of Lung and Pancreatic Tumors

Among the worst malignancies in the world are lung and pancreatic, and effective treatment depends on early identification and precise diagnosis. A powerful technique for analyzing medical images, deep learning, a category of machine learning, has the potential to enhance the identification and characterization of lung and tumors of the pancreas. In this research, this study examines current developments in classification of images, segmentation, and treatment outcome prediction methods for studying lung and pancreatic cancers. Gaussian Mixture model and EM techniques are employed during preprocessing. The model for classification is then used to determine the disease prediction after segmenting the pancreatic and lung tumors. In order to help radiologists discover malignancies early, traditional machine learning methods have demonstrated encouraging results in properly recognizing lung tumors from CT images. This entails defining tumor edges from medical pictures while segmenting pancreatic tumors. Finally, deep learning offers useful methods for early identification, cancer segmentation and outcome prediction, holding significant potential for research into lung and pancreatic malignancies. To guarantee the dependability, accessibility, and generality. of models developed using deep learning in clinical practice, there are some issues that must be resolved.