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Mohammed Safwan

Researcher at Indian Institute of Technology Madras

Publications -  9
Citations -  1725

Mohammed Safwan is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Convolutional neural network & Deep learning. The author has an hindex of 5, co-authored 8 publications receiving 1090 citations.

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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, +438 more
- 05 Nov 2018 - 
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
Journal ArticleDOI

BACH: Grand challenge on breast cancer histology images.

Guilherme Aresta, +35 more
- 01 Aug 2019 - 
TL;DR: The Grand Challenge on Breast Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018) as mentioned in this paper.
Journal ArticleDOI

IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge

Prasanna Porwal, +57 more
- 01 Jan 2020 - 
TL;DR: The set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD), which received a positive response from the scientific community, have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.
Book ChapterDOI

Classification of Breast Cancer Histology Image using Ensemble of Pre-trained Neural Networks

Sai Saketh Chennamsetty, +2 more
TL;DR: An ensemble of convolutional neural networks trained on different pre-processing regimes to classify histology images as Normal, In-situ, Benign or Invasive is made use of.
Book ChapterDOI

Automatic Segmentation and Overall Survival Prediction in Gliomas Using Fully Convolutional Neural Network and Texture Analysis

Varghese Alex, +2 more
TL;DR: In this paper, a Fully Convolutional Neural Network (FCNN) was used for the segmentation of gliomas from Magnetic Resonance Images (MRI) by training a 23 layer deep FCNN on 2-D slices extracted from patient volumes.