With the advancement of Machine Learning (ML) techniques, medical diagnosis has changed rapidly in recent years. Using image recognition and pattern detection medical practitioners and individuals are increasingly using skin cancer apps for early detection of skin cancer. Due to the high prevalence of skin cancer, with around 9,500 new cases diagnosed daily in the U.S., early detection is crucial for successful treatment and can sometimes lead to complete reversal of the condition. This paper explores the use of open-source ML algorithms in Python for processing medical images to predict cancer growth and treatment. We tested many available open-source ML algorithm-based software sets in Python as applied to medical image data processing, and modeling used to predict cancer growth and treatments. We follow a holistic approach to data analysis leading to more efficient cancer detection based upon both cell analysis and image recognition. We compared ML based software methods and analyze their detection accuracy. We improved our ML based models and obtained results using Swin Transformers for better accuracy. In addition, we acquired publicly available data of cancer cell image files and analyzed using deep learning algorithms to detect benign and suspicious image samples. We apply the current pattern matching algorithms and study the available data with possible diagnosis of cancer types.