CanScan: Classification of cancer types using deep learning approach

In this research, an artificial intelligence (AI) model for the detection of tree types of cancer, cluster in benign and malignant, is presented. The main objective is to develop a precise and reliable classification system that can aid in early diagnosis and medical decision-making. The model utilizes deep learning techniques and is based on a carefully selected set of features extracted from medical images and relevant clinical data. To train and evaluate the model, a dataset of clinical data collected from cancer patients across different hospitals and medical centers was used. Data preprocessing techniques were applied to normalize and clean the data before feeding it into the AI model. A classification algorithm, such as a neural network, was implemented and trained using a deep learning approach. The model 'InceptionV3' achieved an 87% accuracy on the validation set and was evaluated using standard performance metrics such as precision and sensitivity. The results obtained demonstrated high accuracy in detecting types of cancer, showcasing the effectiveness of the proposed model. Furthermore, comparisons were made with other existing cancer detection approaches in the scientific literature, and it was observed that the proposed model outperforms several of them in terms of accuracy and overall performance. This study not only represents a significant advancement in the field of cancer detection but also underscores the potential of artificial intelligence to transform the landscape of healthcare. It is expected that this model can enhance the accuracy and efficiency of diagnosis, thereby assisting healthcare professionals in clinical decision-making.