Evaluation of sparsity metrics and evolutionary algorithms applied for normalization of H&amp;E histological images

Tosta, Thaína A. Azevedo; de Faria, Paulo Rogério; Neves, Leandro Alves; Martins, Alessandro Santana; Kaushal, Chetna; do Nascimento, Marcelo Zanchetta

doi:10.1007/s10044-024-01218-7

Pattern Anal Applic

2024

DOI: 10.1007/s10044-024-01218-7

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Evaluation of sparsity metrics and evolutionary algorithms applied for normalization of H&E histological images

Thaína A. Azevedo Tosta,

Paulo Rogério de Faria,

Leandro Alves Neves

et al.

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Cited by 1 publication

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ConjunctiveNet: an improved deep learning-based conjunctive-eyes segmentation and severity detection model

Pahwa,

Kaur,

Dhiman

et al. 2024

IJICC

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PurposeThe study aims to enhance the detection and classification of conjunctival eye diseases' severity through the development of ConjunctiveNet, an innovative deep learning framework. This model incorporates advanced preprocessing techniques and utilizes a modified Otsu’s method for improved image segmentation, aiming to improve diagnostic accuracy and efficiency in healthcare settings.Design/methodology/approachConjunctiveNet employs a convolutional neural network (CNN) enhanced through transfer learning. The methodology integrates rescaling, normalization, Gaussian blur filtering and contrast-limited adaptive histogram equalization (CLAHE) for preprocessing. The segmentation employs a novel modified Otsu’s method. The framework’s effectiveness is compared against five pretrained CNN architectures including AlexNet, ResNet-50, ResNet-152, VGG-19 and DenseNet-201.FindingsThe study finds that ConjunctiveNet significantly outperforms existing models in accuracy for detecting various severity stages of conjunctival eye conditions. The model demonstrated superior performance in classifying four distinct severity stages – initial, moderate, high, severe and a healthy stage – offering a reliable tool for enhancing screening and diagnosis processes in ophthalmology.Originality/valueConjunctiveNet represents a significant advancement in the automated diagnosis of eye diseases, particularly conjunctivitis. Its originality lies in the integration of modified Otsu’s method for segmentation and its comprehensive preprocessing approach, which collectively enhance its diagnostic capabilities. This framework offers substantial value to the field by improving the accuracy and efficiency of conjunctival disease severity classification, thus aiding in better healthcare delivery.

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ConjunctiveNet: an improved deep learning-based conjunctive-eyes segmentation and severity detection model

Pahwa,

Kaur,

Dhiman

et al. 2024

IJICC

View full text Add to dashboard Cite

show abstract

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Evaluation of sparsity metrics and evolutionary algorithms applied for normalization of H&E histological images

Cited by 1 publication

References 42 publications

ConjunctiveNet: an improved deep learning-based conjunctive-eyes segmentation and severity detection model

ConjunctiveNet: an improved deep learning-based conjunctive-eyes segmentation and severity detection model

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