The retinal illness that causes vision loss frequently on the globe is glaucoma. Hence, the earlier detection of Glaucoma is important. In this article, modified AlexNet deep leaning model is proposed to category the ...
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The retinal illness that causes vision loss frequently on the globe is glaucoma. Hence, the earlier detection of Glaucoma is important. In this article, modified AlexNet deep leaning model is proposed to category the source retinal images into either healthy or Glaucoma through the detection and segmentations of optic disc (OD) and optic cup (OC) regions in retinal pictures. The retinal images are preprocessed and OD region is detected and segmented using circulatory filter. Further, OC regions are detected and segmented using K-means classification algorithm. Then, the segmented OD and OC region are classified and trained by the suggested AlexNet deep leaning model. This model classifies the source retinal image into either healthy or Glaucoma. Finally, performance measures have been estimated in relation to ground truth pictures in regards to accuracy, specificity and sensitivity. These performance measures are contrasted with the other previous Glaucoma detection techniques on publicly accessible retinal image datasets HRF and RIGA. The suggested technique as described in this work achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset. AIM: Segmenting the OD and OC areas and classifying the source retinal picture as either healthy or glaucoma-affected. METHODS: The retinal images are preprocessed and OD region is detected and segmented using circulatory filter. Further, OC region is detected and segmented using K-means classification algorithm. Then, the segmented OD and OC region classified are and trained by the suggested AlexNet deep leaning model. RESULTS: The suggested method as described in this work achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achie
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