Colon Cancer Tissue Classification Using ML

Tripathi, Ashish; Kumar, Kuldeep; Misra, Anuradha; Chaurasia, Brijesh Kumar

doi:10.1109/iscon57294.2023.10112181

Cited by 14 publications

(2 citation statements)

References 23 publications

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“…In existing approaches [15][16][17][18], the impact of data preprocessing on learning and result accuracy is an issue. Therefore, accurate glaucoma disease detection and data preprocessing is essential.…”

Section: Related Workmentioning

confidence: 99%

“…The diagnosis of a glaucomatous eye has been made rapid and simple using computer vision and deep learning techniques. Due to its superior removal capabilities and effective computation, ML and CNN has been the best option for image classification over the past ten years [4][5][6], [16][17][18]. AlexNet obtains ground-breaking performance on the ImageNet image categorization at the beginning of deep learning [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Glaucoma Classification using Light Vision Transformer

Singh,

Dev

et al. 2023

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Nowadays one of the primary causes of permanent blindness is glaucoma. Due to the trade-offs, it makes in terms of portability, size, and cost, fundus imaging is the most widely used glaucoma screening technique. OBJECTIVES:To boost accuracy,focusing on less execution time, and less resources consumption, we have proposed a vision transformer-based model with data pre-processing techniques which fix classification problems. METHODS: Convolution is a “local” technique used by CNNs that is restricted to a limited area around an image. Self-attention, used by Vision Transformers, is a “global” action since it gathers data from the whole image. This makes it possible for the ViT to successfully collect far-off semantic relevance in an image. Several optimizers, including Adamax, SGD, RMSprop, Adadelta, Adafactor, Nadam, and Adagrad, were studied in this paper. We have trained and tested the Vision Transformer model on the IEEE Fundus image dataset having 1750 Healthy and Glaucoma images. Additionally, the dataset was preprocessed using image resizing, auto-rotation, and auto-adjust contrast by adaptive equalization. RESULTS: Results also show that the Nadam Optimizer increased accuracy up to 97% in adaptive equalized preprocessing dataset followed by auto rotate and image resizing operations. CONCLUSION: The experimental findings shows that transformer based classification spurred a revolution in computer vision with reduced time in training and classification.

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Section: Related Workmentioning

confidence: 99%