Background The ever-growing need for cheap, simple, fast, and accurate healthcare solutions spurred a lot of research activities which are aimed at the reliable deployment of artificial intelligence in the medical fields. However, this has proved to be a daunting task especially when looking to make automated diagnoses using biomedical image data. Biomedical image data have complex patterns which human experts find very hard to comprehend. Against this backdrop, we applied a representation or feature learning algorithm: Invariant Scattering Convolution Network or Wavelet scattering Network to retinal fundus images and studied the the efficacy of the automatically extracted features therefrom for glaucoma diagnosis/detection. The influence of wavelet scattering network parameter settings as well as 2-D channel image type on the detection correctness is also examined. Our work is a distinct departure from the usual method where wavelet transform is applied to pre-processed retinal fundus images and handcrafted features are extracted from the decomposition results. Here, the RIM-ONE DL image dataset was fed into a wavelet scattering network developed in the Matlab environment to achieve a stage-wise decomposition process called wavelet scattering of the retinal fundus images thereby, automatically learning features from the images. These features were then used to build simple and computationally cheap classification algorithms. Results Maximum detection correctness of 98% was achieved on the held-out test set. Detection correctness is highly sensitive to scattering network parameter setting and 2-D channel image type. Conclusion A superficial comparison of the classification results obtained from our work and those obtained using a convolutional neural network underscores the potentiality of the proposed method for glaucoma detection.

The ever-growing need for cheap, simple, fast, and reliable healthcare solutions spurred a lot of research activities that are aimed at the reliable deployment of artificial intelligence in the medical fields. However, this has proved to be a daunting task especially when looking to make automated diagnoses using biomedical image data. Biomedical image data have complex patterns which human experts find very hard to comprehend. This paper studies the efficacy of wavelet scattering features from retinal fundus images for automatic glaucoma diagnosis/detection. The influence of wavelet image scattering network parameter settings as well as 2-D channel image representation type on the detection correctness is also examined. The wavelet image scattering network developed in the Matlab environment was used on the RIM-ONE DL image dataset to execute the scattering decomposition and obtain the scattering coefficients. Features from the coefficients were then used to build simple classification algorithms. Maximum detection correctness of 98% was achieved on the held-out test set. Results showed that detection correctness is highly sensitive to scattering network parameter settings and 2-D channel representation type. A superficial comparison of the classification results obtained from the proposed method and those obtained using a convolutional neural network underscores the potentiality of our method.