In this work we propose a methodology for an automatic food classification system which recognizes the contents of the meal from the images of the food. We developed a multi-layered deep convolutional neural network (CNN) architecture that takes advantages of the features from other deep networks and improves the efficiency. Numerous classical handcrafted features and approaches are explored, among which CNNs are chosen as the best performing features. Networks are trained and fine-tuned using preprocessed images and the filter outputs are fused to achieve higher accuracy. Experimental results on the largest real-world food recognition database ETH Food-101 and newly contributed Indian food image database demonstrate the effectiveness of the proposed methodology as compared to many other benchmark deep learned CNN frameworks.Comment: 5 pages, 3 figures, 3 tables, IEEE Signal Processing Letter

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Of all the skin cancer that is prevalent, melanoma has the highest mortality rates. Melanoma becomes life threatening when it penetrates deep into the dermis layer unless detected at an early stage, it becomes fatal since it has a tendency to migrate to other parts of our body. This paper presents an automated non-invasive methodology to assist the clinicians and dermatologists for detection of melanoma. Unlike conventional computational methods which require (expensive) domain expertise for segmentation and hand crafted feature computation and/or selection, a deep convolutional neural network based regularized discriminant learning framework which extracts low dimensional discriminative features for melanoma detection is proposed. Our approach minimizes the whole of within-class variance information and maximizes the total class variance information. The importance of various subspaces arising in the within-class scatter matrix followed by dimensionality reduction using total class variance information are analyzed for melanoma detection. Experimental results on ISBI 2016, MED-NODE, PH2 and the recent ISBI 2017 databases show the efficacy of our proposed approach as compared to other state-of-the-art methodologies.

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In this letter, we propose a multi-level dual-attention model to classify two common macular diseases, age-related macular degeneration (AMD) and diabetic macular edema (DME) from normal macular eye conditions using optical coherence tomography (OCT) imaging technique. Our approach unifies the dual-attention mechanism at multi-levels of the pre-trained deep convolutional neural network (CNN). It provides a focused learning mechanism by taking into account both multi-level features based attention focusing on the salient coarser features and self-attention mechanism attending higher entropy regions of the finer features. Our proposed method enables the network to automatically focus on the relevant parts of the input images at different levels of feature subspaces. This leads to a more locally deformation-aware feature generation and classification. The proposed approach does not require pre-processing steps such as extraction of region of interest, denoising and retinal flattening, making the network more robust and fully automatic. Experimental results on two macular OCT databases show the superior performance of our proposed approach as compared to the current state-of-the-art methodologies.