Enhancing Lung Cancer Diagnosis with ResNet50: Superior Accuracy in Classifying Squamous Cell Carcinoma, Adenocarcinoma, and Benign Tissue

Enhancing results for patients requires early and precise lung cancer identification. The current investigates the sophisticated use of machine learning techniques, using the ResNet50 convolutional neural network, in particular, to transform the identification and categorization of tissue from the lungs into three separate groups: lung squamous cell carcinoma, lung adenocarcinoma, and benign lung tissue. Our technique obtains great accuracy as well as durability for differentiating these essential types of tissues by taking use of ResNet50’s depth and architecture, which permits the training of very deep networks without the vanishing gradient problem. A large dataset of annotated medical photos is used for training and validating the machine learning algorithm, which performs better than conventional diagnosis techniques. According to these results, ResNet50 integration can greatly improve accuracy in diagnosis in medical imaging processes, opening the door for earlier intervention and tailored therapies in the treatment of cancer of the lungs. This study highlights the opportunity of deep machine learning to advance customised healthcare and its potential for transformation to medical diagnostics.