Journal of Biomedical Sciences

  • ISSN: 2254-609X
  • Journal h-index: 14
  • Journal CiteScore: 5.48
  • Average acceptance to publication time (5-7 days)
  • Average article processing time (30-45 days) Less than 5 volumes 30 days
    8 - 9 volumes 40 days
    10 and more volumes 45 days
20+ Million Readerbase
Indexed In
  • Genamics JournalSeek
  • China National Knowledge Infrastructure (CNKI)
  • Directory of Research Journal Indexing (DRJI)
  • OCLC- WorldCat
  • Google Scholar
  • Secret Search Engine Labs
Share This Page


Brain tumor detection using transfer learning

Syed Afsar Ali Shaha*

In recent times, brain tumours are considering the most threatening disease for human health and life. The mortality rate of brain tumor amongst all cancerous diseases is 7%, which is quite high. The medical image analysis becomes more challenging due to complex architecture of the numerous brain tumours; which are irregular in shape and size. Their overlapping tissues with healthy brain tissues further make it extremely difficult to identify their presence. Similarly determining their type and size further expedite the complexity of analysis procedure. Traditional machines learning methods involving human intervention are not adequately producing the optimal results, time consuming and rely on human experts. Deep learning models provide fully automated models and overcome the limitation of traditional procedure. Deep learning models provide adequate mechanism to identify tumor, its type and size adequately. Our proposed R-CNN along with the transfer learning mechanism between Random Forest and Support Vector Machine (SVM) yields competitive results. The experiment is performed over MRI modalities using python open CV libraries. Deep learning fully automated analysis provide adequate and timely statistics about tumor, that helps medical expert to timely initiate the adequate treatment, which is a significant towards saving human life


Brain tumor identification; R-CNN; Transfer Learning; Random Forest and SVM; Hybrid model

Published Date: 2022-12-30; Received Date: 2022-11-22