Categorizing Dermatological Malignancies Via Computational Methods
Jahnavi Raghava Singh1, J Gopi2,V.Anil Santosh3, DDD Suri Babu4
1M.Tech Student, Department of CSE, ISTS Engineering College, Rajahmundry, India
2Associate Professor, Department of CSE, ISTS Engineering College, Rajahmundry, India
3Associate Professor, Department of CSE, ISTS Engineering College, Rajahmundry, India
4Associate Professor, Department of CSE, ISTS Engineering College, Rajahmundry, India
ABSTRACT
In this study, a machine learning model is developed to classify different types of cancer using convolutional neural networks (CNNs) for image processing. The core objective is to achieve a performance level comparable to that of dermatologists. The model is trained on a substantial dataset of medical images, enabling it to learn and recognize various characteristics indicative of different cancer types. By leveraging the power of CNNs, the model can process these images effectively, identifying subtle patterns and features that are often challenging to detect with the naked eye. The training process involves feeding the CNN with labelled images, enabling it to differentiate between benign and malignant cases with high accuracy. Through rigorous testing, the model demonstrates competence on par with experienced dermatologists, both in terms of sensitivity and specificity. This equivalence in performance is particularly significant as it underscores the model's potential to aid in clinical settings, providing reliable second opinions and enhancing diagnostic workflows. A user interface is also developed to allow input images to be analysed by the trained CNN model. This interface not only displays the model’s predictions but also provides essential metrics such as confidence scores and probability distributions. These metrics offer valuable insights into the model's decision-making process, aiding clinicians in understanding and trusting the AI's assessments. Overall, the findings suggest that convolutional neural networks hold substantial promise for improving cancer diagnosis. The model's high performance in classification tasks demonstrates its viability as a tool for supporting dermatologists in clinical practice. By reducing diagnostic errors and accelerating the identification process, this technology has the potential to significantly impact patient outcomes and advance the field of medical imaging and diagnostics.
Keywords: Convolutional Neural Networks (CNNs); Cancer Classification; Medical Image Processing; Dermatology AI; Diagnostic Accuracy
