In this study, exploratory deep feature engineering using convolutional neural network (CNN) on histological manifold has been proposed for robust breast carcinoma detection. A comparative evaluation emphasizing the adequacy of manifold learning and CNN aided deep features over state-of-the-art biomarkers and other deep learning models is done for histopathological image (HI) classification. The proposed framework efficiently differentiate the spatial textural non-stationarity in HI and apprehend the topographic aberrations of cancerous tissues and exemplifies its competency for clinical settings deployment in developing countries. Experimental results are discussed in detail.
A variational mode decomposition (VMD) and local binary patterns (LBP) based features extraction from digital fundus images is proposed for glaucoma detection. The band-limited intrinsic mode images (BLIM’s) obtained by VMD, encompasses the varying spectral content embodying the non-linear and spatial non-stationary textural modulations in the fundus images. LBP feature descriptors apprehend the topographic tortuousness of the optical tissue fluids and substantiate the perturbations in intraocular fluid pressure (IOP) within the human eye which is caused due to glitches in the optical drainage system. Using artificial neural network, a classification accuracy of 95.2% is obtained on publicly available Medical Image Analysis Group (MIAG) dataset, which validates the suitability of the proposed framework in glaucoma identification.
In this contribution, intelligent identification of glaucoma from digital fundus images using stacked sparse autoencoder (SSAE) is proposed. The fundus images are initially converted to gray-scale and normalized w.r.t., background illuminance while maintaining contrast constancy across the dataset. Unfolded feature vectors from the pre-processed with proper rescaling and grays-scale converted fundus images are fed to SSAE for learning efficient feature representation and classification thereof using a softmax layer. A comparative evaluation highlighting the superiority of SSAE method with existing state-of the art techniques is presented to validate its efficacy in glaucoma detection. The proposed framework can be used as a clinical decision support system assisting ophthalmologists in confirming their diagnosis with high reliability and accuracy.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.