Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks

Recognizing Traffic Signs using intelligent systems can drastically reduce the number of accidents happening world-wide. With the arrival of Self-driving cars it has become a staple challenge to solve the automatic recognition of Traffic and Hand-held signs in the major streets. Various machine learning techniques like Random Forest, SVM as well as deep learning models has been proposed for classifying traffic signs. Though they reach state-of-the-art performance on a particular data-set, but fall short of tackling multiple Traffic Sign Recognition benchmarks. In this paper, we propose a novel and one-for-all architecture that aces multiple benchmarks with better overall score than the state-of-the-art architectures. Our model is made of residual convolutional blocks with hierarchical dilated skip connections joined in steps. With this we score 99.33% Accuracy in German sign recognition benchmark and 99.17% Accuracy in Belgian traffic sign classification benchmark. Moreover, we propose a newly devised dilated residual learning representation technique which is very low in both memory and computational complexity

Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images

Diagnosing different retinal diseases from Spectral Domain Optical Coherence Tomography (SD-OCT) images is a challenging task. Different automated approaches such as image processing, machine learning and deep learning algorithms have been used for early detection and diagnosis of retinal diseases. Unfortunately, these are prone to error and computational inefficiency, which requires further intervention from human experts. In this paper, we propose a novel convolution neural network architecture to successfully distinguish between different degeneration of retinal layers and their underlying causes. The proposed novel architecture outperforms other classification models while addressing the issue of gradient explosion. Our approach reaches near perfect accuracy of 99.8% and 100% for two separately available Retinal SD-OCT data-set respectively. Additionally, our architecture predicts retinal diseases in real time while outperforming human diagnosticians.Comment: 8 pages. Accepted to 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019

RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget (Student Abstract)

Learning High-Resolution representations is essential for semantic segmentation. Convolutional neural network (CNN) architectures with downstream and upstream propagation flow are popular for segmentation in medical diagnosis. However, due to performing spatial downsampling and upsampling in multiple stages, information loss is inexorable. On the contrary, connecting layers densely on high spatial resolution is computationally expensive. In this work, we devise a Loose Dense Connection Strategy to connect neurons in subsequent layers with reduced parameters. On top of that, using a m-way Tree structure for feature propagation we propose Receptive Field Chain Network (RFC-Net) that learns high-resolution global features on a compressed computational space. Our experiments demonstrates that RFC Net achieves state-of-the-art performance on Kvasir and CVC-ClinicDB benchmarks for Polyp segmentation. Our code is publicly available at github.com/sourajitcs/RFC-NetAAAI23

A saponin-polybromophenol antibiotic (CU1) from Cassia fistula Bark Against Multi-Drug Resistant Bacteria Targeting RNA polymerase

Background: Gradual increase of multidrug resistant infections is a threat to the human race as MDR plasmids have acquired.>10 mdr and drug efflux genes to inactivate antibiotics. Plants secret anti-metabolites to retard growth of soil and water bacteria and are ideal source of antibiotics. Purpose: Purpose of the study is to discover an alternate phyto-drug from medicinal plants of India that selectively kills MDR bacteria. Methods: MDR bacteria isolated from Ganga river water, milk, chicken meat and human hair for testing phyto-extracts. Eighty medicinal plants were searched and six phyto-extracts were selected having good antibacterial activities as demonstrated by agar-hole assays giving 15 ​mm or greater lysis zone. Phyto-extracts were made in ethanol or methanol (1:5 w/v) for overnight and were concentrated. Preparative TLC and HPLC were performed to purify phytochemical. MASS, NMR, FTIR methods were used for chemical analysis of CU1. In vitro RNA polymerase and DNA polymerase assays were performed for target identification. Results: CU1 belongs to a saponin bromo-polyphenol compound with a large structure that purified on HPLC C18 column at 3min. CU1 is bacteriocidal but three times less active than rifampicin in Agar-hole assay. While in LB medium it shows greater than fifteen times poor inhibitor due to solubility problem. CU1 inhibited transcription from Escherichia coli as well as Mycobacterium tuberculosis RNA Polymerases. Gel shift assays demonstrated that CU1 interferes at the open promoter complex formation step. On the other hand CU1 did not inhibit DNA polymerase. Conclusion: Phyto-chemicals from Cassia fistula bark are abundant, less toxic, target specific and may be a safer low cost drug against MDR bacterial diseases