Paper Title
Carcinikos - Presentiment of Cancer in Males Using Multimodal Techniques

Abstract
Lung and prostate cancer don’t exhibit observable symptoms until the disease is well along its course, and even then, they are frequently disregarded. In the initial stages of the disease, these cancer nodules are very small and cannot be seen with the naked eye thereby resulting in a delayed diagnosis of the illness. The proposed methodology makes use of a modified CNN with multiple layers comprising a convolutional layer, a custom pooling layer where randomness is introduced into training by selecting max pooling or average pooling according to the distribution of values in the pooling region and a softmax layer along with an updated LReLU with leakage correction, for detecting the presence of lung cancer. This procedure has been applied on images used for medical purposes, including MRIs scans for lung cancer. By analyzing these images the proposed methodology aims to enhance accuracy and reduce false positives and false negatives. Random Forest model using Decision Tree Classifier is proposed for detecting the presence of prostate cancer by utilizing various clinical parameters. The proposed work has the potential to revolutionize cancer diagnosis, leading to improved treatment outcomes and reduced disease burden in male patients. Keywords - Medical AI, CNN’s, Random Forest , lung cancer, prostate cancer, machine learning, early detection.