Enhancing Cancer Detection with Machine Learning and Deep Learning: A Focus on Breast and Skin Cancer
Mudit Singal, Garima Jain, Nidhi Sharma, Shivam Anand, Md. Maaz Raza, Om Tiwari · 2024
Machine learning (ML) and deep learning (DL) are transforming cancer detection, offering significant improvements in early diagnosis and treatment. These technologies analyze diverse data sources to enhance accuracy, efficiency, and accessibility in cancer care. ML models have achieved remarkable accuracy in cancer detection, reaching around 96 percent sensitivity and specificity, surpassing traditional methods. By analyzing large-scale datasets, these models detect subtle patterns and biomarkers indicative of cancer, enabling earlier intervention. They also facilitate personalized medicine by developing tailored strategies based on individual patient characteristics. Automating cancer detection tasks streamlines clinical workflows, reduces diagnostic turnaround times, and eases the burden on healthcare professionals. Ongoing research continues to refine DL architectures, enabling the processing of multi-modal data and capturing complex disease patterns. The integration of multiomic data, including genomics, proteomics, and imaging, enhances our understanding of cancer biology, leading to new biomarkers and therapeutic targets. This comprehensive approach promises to revolutionize cancer diagnosis and treatment, potentially achieving an overall accuracy of around 80 percent. In conclusion, ML and DL integration in cancer detection represents a significant advancement in medical research and clinical practice, with far-reaching implications for improving patient care and outcomes.