• CNN and ANN in Cancer Research: Pioneering AI Solutions for Early Detection and Treatment
  • Helia Sepahvand,1,* Sarina Roshani,2 Diana Sedaghatnia,3 Narges Safari,4 Majedeh Mortazavi,5 Hesameddin Akbarein,6
    1. DVM Student, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran.
    2. DVM Student, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran.
    3. DVM Student, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran.
    4. DVM Student, Faculty of Veterinary Medicine, Garmsar Branch, Islamic Azad University, Garmsar, Iran.
    5. DVM Student, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran.
    6. Division of Epidemiology & Zoonoses, Department of Food Hygiene & Quality Control, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran


  • Introduction: Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) are very powerful Artificial Intelligence (AI) methods that are changing the way healthcare is provided. The ANN can be used for a wide range of tasks, including regression, pattern recognition, and classification. CNN is used for things like computer vision and picture detection. Putting these technologies into medical gadgets like thermometers, imaging systems, and wearable monitors could help doctors make more accurate diagnoses and do their jobs faster. Cancer studies that use CNNs and ANNs should be able to find cancer earlier, make treatment more specific, and lower death rates over time. This article reviews how CNNs and ANNs can be used in cancer research, including how they can help with early diagnosis and planning treatment, as well as the issues that come up when they are used.
  • Methods: We looked at new studies to find out how CNNs and ANNs are being used to find and treat cancer better. The studies we looked at were from 2022 to 2024 and were reviewed by experts in the field. Some keywords, like "AI in oncology," "CNN cancer detection," and "ANN cancer treatment," were used to look through sources like Google Scholar and PubMed. Titles and descriptions were looked at to see if they were relevant, and full texts of some studies were read to see if they used the right methods and added much to the field.
  • Results: It is now possible to find cancer treatments that hurt healthy parts the least, thanks to genomics and biological markers. These problems could be fixed with deep learning systems that can look at medical images, genomic data, and patient information on their own. ANNs and CNNs are two types of AI-driven models that can quickly and correctly look at huge amounts of data. We can learn a lot from this about how cancer grows, how well medicines work, and how well people do. The CNN is a type of deep learning system that works with organized grid data, like pictures of illness. They can find things, sort them into groups, and figure out what a picture is about very quickly. CNNs can find tumors early on that other imaging methods might miss when they look at CT pictures. Images taken with a dermoscopy can help CNNs tell the difference between skin tumors that are not dangerous and those that are. There are more ways to use ANNs, and they can be used with different kinds of data, such as DNA, RNA, and clinical data. That's why these tools are used to find connections between changes in genes, how the cancer grows, and how well people do. An important thing that can be done with ANNs is to guess how cancer meds will work. This helps doctors figure out the best way to treat each patient. Another interesting area of cancer research is using ANNs to find new drugs. These computers can look through huge lists of chemicals to find possible cures for cancer. But there are some problems that need to be fixed. The kind and amount of data needed to train these models well, how easy it is to understand them, and the idea that AI could replace human knowledge are some of the things that worry people.
  • Conclusion: AI programs are being worked on to do things that are similar to what professionals do. This will make it possible to do quick, accurate tests that lead to results in real-time. This change makes it easier to find skin cancer early, so there is less need for invasive treatments. By cutting down on the time it takes to do exams, these ideas can improve healthcare services, especially in places where there aren't any doctors or emergencies. CNNs and ANNs are new ways to find, diagnose, and treat cancer early. We need to fix issues like insufficient data, models that are hard to understand, and ethical concerns before we can merge into clinical practice. AI-powered solutions can change how cancer is handled and make it less of a problem around the world. They are what will make cancer studies go forward.
  • Keywords: AI, oncology, CNNs, and ANNs