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SCRNN: A Deep Model for Colorectal Cancer Classification From Histological Images - Implementation Using TensorFlow
Abstract
Colorectal cancer holds a prominent place on the global health landscape. Its early detection is crucial for successful patient outcomes. Histological analysis of tissue samples plays an indispensable role in diagnosing and classifying colorectal cancer. Accurate classification is paramount, as it influences the choice of treatment and patient prognosis. This chapter investigates the statistics surrounding colorectal cancer, its vital role in the healthcare sector, and the transformative potential of artificial intelligence in automating its diagnosis. This chapter proposes a ShuffleNetV2-CRNN (SCRNN), a novel deep learning architecture designed for colorectal cancer classification from histological images. SCRNN combines the efficiency of ShuffleNetV2 for feature extraction with the context-awareness of a convolutional-recurrent neural network for precise classification. SCRNN is evaluated against chosen deep models – Simple CNN, vGG16, ResNet-18, and MobileNet. Experimental results demonstrate appreciable performance of SCRNN across a diverse range of tissue types.
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