Brain tumor segmentation from Magnetic Resonance Imaging (MRI) is of great importance for better tumor diagnosis, growth rate prediction and radiotherapy planning. But this task is extremely challenging due to intrinsically heterogeneous tumor appearance, the presence of severe partial volume effect and ambiguous tumor boundaries. In this work, a unique approach of tumor segmentation is introduced based on superpixel level features extracted from all three planes (x-y, y-z, and z-x) of 3D volumetric MR images. In order to avoid the pixel randomness and to account for precise inhomogeneous boundaries of brain tumor, each of the images belonging to a particular plane is partitioned into irregular patches (superpixels) based on their intensity and spatial similarity. Next, various statistical and textural features are extracted from each superpixel where all three planes are considered separately in order to obtain better labeling on superpixels in tumor edges. A feature selection scheme is proposed based on their performance on histogram based consistency analysis and local descriptor pattern analysis, which offers a significant reduction in feature dimension without sacrificing classification performance. For the purpose of supervised classification, Extremely Randomized Trees is used to classify these superpixels into a tumor or a non-tumor class. Finally, pixel level decision is taken based on corresponding decisions obtained in each plane. Extensive simulations are carried out on publicly available dataset and it is found that the proposed method offers better tumor segmentation performance in comparison to that obtained by some state of the art methods.

Since the end of 2019, Novel Corona Virus Disease (COVID-19) has brought about a plethora of unforeseen changes to the world as we know it. Despite our ceaseless fight against it, COVID-19 has claimed millions of lives, and the death toll exacerbated due to its extremely contagious and fast-spreading nature. To control the spread of this highly contagious disease, a rapid and accurate diagnosis can play a very crucial part. Motivated by this context, a parallelly concatenated convolutional block-based capsule network is proposed in this paper as an efficient tool to diagnose the COVID-19 patients from multimodal medical images. Concatenation of deep convolutional blocks of different filter sizes allows to integrate discriminative spatial features by simultaneously changing the receptive field and enhances the scalability of the model. Moreover, concatenation of capsule layers strengthens the model to learn more complex representation by presenting the information in a fine to coarser manner. The proposed model is evaluated on three benchmark datasets in which two of them are chest radiographs datasets and the rest is an ultrasound imaging dataset. The architecture which we have proposed through extensive analysis and reasoning achieved outstanding performance in COVID-19 detection task which signifies the potentiality of the proposed model. The implementation of this proposed method is made publicly available in github.Impact Statement-Suffering from elongated testing time and less sensitivity, the traditional diagnostic test based COVID-19 diagnosis schemes such as Molecular and Antigen tests usually require manual medical image analysis for further investigation. Hence, an automated process is desired to deal with the problem of diagnosing the increasing number of COVID-19 patients worldwide. However, automating the diagnosing process is an onerous task because of the unavailability of high-resolution medical images and the characteristical similarity of COVID-19 and other viral and bacterial diseases that can eventually lead to misinterpretation of data. The proposed scheme introduces a modified capsule network (CapsCovNet) by addressing all of these issues which provides a significant improvement in COVID-19 diagnosis from different imaging methods (Ultrasound Chest X-ray) compared to the previous state-of-the-art models. Moreover, it can be an efficient tool that can facilitate the COVID-19 diagnosis process by providing invaluable information to medical practitioners.

Tuberculosis (TB) is a communicable disease that is one of the top 10 causes of death worldwide according to the World Health Organization [1]. Hence, Early detection of Tuberculosis is an important task to save millions of lives from this life threatening disease. For diagnosing TB from chest X-Ray, different handcrafted features were utilized previously and they provided high accuracy even in a small dataset. However, at present, deep learning (DL) gains popularity in many computer vision tasks because of their better performance in comparison to the traditional manual feature extraction based machine learning approaches and Tuberculosis detection task is not an exception. Considering all these facts, a cascaded ensembling method is proposed that combines both the hand-engineered and the deep learningbased features for the Tuberculosis detection task. To make the proposed model more generalized, rotationinvariant augmentation techniques are introduced which is found very effective in this task. By using the proposed method, outstanding performance is achieved through extensive simulation on two benchmark datasets that verifies the effectiveness of the method.

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