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Traditional one class support tensor machine (OCSTM) is a popular classifier that is widely adopted for one class classification, however, outliers in the data negatively affects its performance. To improve the robustness of OCSTM against outliers, in this paper, we present OCSTM with bounded loss function rather than finding optimized support vectors with unbounded loss function. To solve the corresponding optimization problem, we have presented half quadratic optimization to drive the problem to traditional OCSTM, followed by solving a typical OCSTM optimization problem iteratively. We further demonstrate our algorithms through experiments on eight real-world benchmark datasets. Experimental results show that the proposed approach separates well most of the samples of interested class from origin even in the presence of outliers.

Automatic classification of skin disease is a challenging task due to high similarity among them as well as complex nature of skin images i.e. hair, skin and nails and illumination conditions. It is one of the most common malignancy that becomes more difficult to treat and potential disfigurement or even death, if not diagnosed early. In this paper, we present a fully automatic skin lesion classification by leveraging three stage deep residual neural networks that is trained end-to-end and does not rely on prior knowledge of the data. Extensive experiment on ISIC-2018 challenges shows that our methods showed considerable improvement (96.07%) in the classification of skin melanoma by not only super-passing the ISIC-2018 challenges as well state of the art methods.

Accurate automatic segmentation of the retinal vessels is crucial for early detection and diagnosis of vision-threatening retinal diseases. This paper presents a lightweight convolutional neural network termed as Shallow Vessel Segmentation Network (SVSN) for vessel segmentation. To achieve semantic segmentation encoder-decoder structures embedded with spatial pyramid pooling modules are used. After checking the input features with pooling through multiple fields of view and rates, it becomes easy for the erstwhile networks to encode multi-scale contextual information. While boundaries for sharper objects are captured by the prevalent networks. Moreover, the need for pre- and post-processing steps are eradicated. Consequently, the detection accuracy is significantly improved with scores of 0.9625 and 0.9645 on DRIVE and STARE datasets respectively.

Accurate automatic segmentation of the retinal vessels is crucial for early detection and diagnosis of vision-threatening retinal diseases. A new supervised method using a variant of the fully convolutional neural network is pro-posed with the advantages of reduced hyper-parameters, reduced computational/memory requirements, and robust performance in capturing tiny vessel information. The fully convolutional architectures previously employed for vessel segmentation have multiple tunable hyperparameters and difficulty in end-to-end training due to their decoder structure. We resolve this problem by sharing information from the encoder for upsampling at the decoder stage, resulting in a significantly smaller number of tunable parameters and low computational overhead at the train and test stages. Moreover, the need for pre- and post-processing steps are eradicated. Consequently, the detection accuracy is significantly improved with scores of 0.9620, 0.9623, and 0.9620 on DRIVE, STARE, and CHASE_DB1 datasets respectively.