• Sabarisuganya.G
• Sasirekha R
• Nandhini A
• Nithyasri M

Histopathological study provides microscopic insights into tissue morphology and is essential for both cancer diagnosis and prognosis. Utilizing cutting-edge deep learning methods, especially convolutional neural networks (CNNs), has shown promise in automating the identification of cancer from histopathology pictures. This project provides a thorough review of the approaches and developments in applying deep learning to this use case. In order to identify areas of interest (ROIs), pathologists first acquire and preprocess data. They next standardize and annotate histopathological and brain MRI images. In order to reduce data scarcity and enhance generalization, various strategies such as transfer learning and data augmentation are utilized during the model training process. We are able to put the Sequential Framework CNN algorithm into practice. Deep learning models, once trained, segment histopathological pictures into non-cancerous and malignant regions using learnt features. Model performance is evaluated using metrics like accuracy, precision, recall, and AUC-ROC; validation techniques make sure the model can be applied to new sets of data. Collaboration with healthcare providers and strict clinical validation are necessary for integration into clinical procedures. Notwithstanding the advancements, many obstacles persist, such as the interpretability of deep learning models, image quality variability, unequal distribution of classes, and ethical problems. Prospective study avenues encompass augmenting interpretability, amalgamating multi-modal data, and tackling privacy apprehensions via federated learning.

Deep learning, Multi type cancer, Convolutional neural network, Histopathological image, MRI image.

"Comprehensive Cancer Detection From Histopathological Images Through Advanced Deep Learning Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.n27-n34, April-2024, Available :http://www.jetir.org/papers/JETIR2404D04.pdf

"Comprehensive Cancer Detection From Histopathological Images Through Advanced Deep Learning Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppn27-n34, April-2024, Available at : http://www.jetir.org/papers/JETIR2404D04.pdf