Prostate Cancer Cell Prediction from Histopathological Images using Convolutional Neural Network
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2022
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In cancer research, pinpointing a patient's future response to treatment is crucial for making informed decisions. This study delves into a potential method for predicting the return of prostate cancer after surgery, utilizing imagery from tissue samples. Scientists analyzed a group of patients, categorizing them based on whether their cancer returned after treatment. To account for existing variations besides the cancer itself, they meticulously matched patients within the group based on factors like age, ethnicity, and disease severity. The proposed technique hinges on a sophisticated form of artificial intelligence known as deep learning. Uniquely, it employs two distinct AI models: one to pinpoint individual cells within the tissue, even in dense areas, and another to classify these cells. By analyzing these classified cells, the researchers were able to estimate, with promising accuracy, the likelihood of cancer recurrence in a patient. This method, if further validated, holds the potential to revolutionize treatment decisions for prostate cancer, offering a more personalized approach. The broader implications of this research extend beyond prostate cancer. This approach, with further development, might be adaptable to predicting treatment outcomes in various cancers. We have found the accuracy 93.33 percent. Keywords- Prostate; Cancer; Convolutional Neural Network; CNN; Deep Learning, prediction
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Electrical and Computer Engineering
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North South University