A review on a deep learning perspective in brain cancer classification

AWorld Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, andWilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm

On the non-abelian Brumer-Stark conjecture and the equivariant Iwasawa main conjecture

We show that for an odd prime p, the p-primary parts of refinements of the (imprimitive) non-abelian Brumer and Brumer-Stark conjectures are implied by the equivariant Iwasawa main conjecture (EIMC) for totally real fields. Crucially, this result does not depend on the vanishing of the relevant Iwasawa mu-invariant. In combination with the authors' previous work on the EIMC, this leads to unconditional proofs of the non-abelian Brumer and Brumer-Stark conjectures in many new cases.Comment: 33 pages; to appear in Mathematische Zeitschrift; v3 many minor updates including new title; v2 some cohomological arguments simplified; v1 is a revised version of the second half of arXiv:1408.4934v

Incidence of post-harvest disease and airborne fungal spores in a vegetable market

The sampling of bioaerosols has been carried out using a Rotorod sampler as well as by exposing culture plates. The screening of some common vegetables was also done for the isolation of fungi as market pathogens to study post-harvest diseases. Altogether, fifty nine fungal spore types and 78 species of 33 genera belonging to different groups were recorded respectively on the rotorod strips and on exposed Petri dishes. Many saprophytic and pathogenic fungi were found to be associated with sampled vegetables from the market. In all forty-six fungal species belonging to 26 genera were recovered from five varieties of vegetables collected from the same market. The most dominant forms of fungi were of Aspergillus followed by Cladosporium, Penicillium, Alternaria, Fusarium, Curvularia, Trichoderma, and Rhizopus. Aspergillus niger, A. flavus, A. fumigatus, Penicillium spp. and Cladosporium herbarum, found to be dominant during the period of investigation. Important mycotoxin-producing fungi such as A. flavus, A. fumigatus and Fusarium moniliforme were isolated from the vegetables collected from the market