International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 07 | Jul 2025
p-ISSN: 2395-0072
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Robust Brain Tumor Recognition in Medical Imaging Using Dense Connectivity and Transfer Learning Anshul Saxena*, Prof. Neha Khare** *Research Scholar Department of CSE, Takshshila Institute of Engineering & Technology, Jabalpur, M.P. **Prof., Department of CSE, Takshshila Institute of Engineering & Technology, Jabalpur, M.P. -------------------------------------------------------------------------***------------------------------------------------------------------------Abstract- The core objective is to develop a reliable and accurate system capable of identifying depressed and nondepressed users through natural language processing (NLP) techniques. To this end, we experimented with a range of sequence modelling approaches, including Simple RNN, Unidirectional and Bidirectional LSTM, Gated Recurrent Units (GRU), and their bidirectional counterparts. Furthermore, we integrated a transfer learning approach using BERT (Bidirectional Encoder Representations from Transformers) to analyses complex sentence structures and contextual relationships in user posts. All models were trained and evaluated on a labelled dataset consisting of social media text entries annotated for depression. The evaluation metric of choice was Recall, given the critical importance of minimizing false negatives in mental health detection. The BERT model achieved the highest recall score of 98.10%, followed closely by Bidirectional LSTM and Bidirectional GRU models, both scoring 97.76%. These results demonstrate the superiority of bidirectional architectures in capturing contextual semantics and underline the importance of transfer-based models like BERT in improving mental health classification performance. Our study confirms that bidirectionality and contextual embedding’s significantly boost detection capabilities in NLPbased mental health applications. This work contributes to building intelligent systems for early diagnosis of depression, supporting mental health professionals with enhanced screening tools in online environments.
other parts of the body [1], [2], [3]. Brain tumors are graded into four different categories:
Keywords: Depression Detection, Social Media Analysis, Natural Language Processing (NLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Models, BERT.
Recent advancements in vision models, such as Vision Language Models (VLLMs) and other emerging technologies, have the potential to significantly enhance the performance of brain tumor detection methods. VLLMs, for instance, combine vision and language processing, allowing for a more nuanced understanding of both visual data and associated medical narratives. Incorporating these models could improve our system’s ability to interpret complex MRI scans in the context of clinical documentation, patient history, or radiology reports. Furthermore, other emerging technologies, such as self-supervised learning and attention mechanisms, are showing promise in enhancing the ability of models to learn
I. INTRODUCTION An uncontrolled growth of brain tissues is a brain Tumor. It produces pressure in the skull and interferes with the brain’s natural functioning. Brain Tumor comes in two different types: Benign (non-cancerous) and Malignant (cancerous). Among them, malignant tumours grow quickly in the brain, damage the normal tissues, and may replicate themselves in
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Grade I: These tumors do not spread quickly and develop slowly. These are connected to a higher chance of enhanced order and may be surgically eliminated nearly entirely. One such tumor is a pilocytic astrocytoma. Grade II: Although they may migrate to surrounding tissues and advance to higher grades, these tumors also grow over time. Grade III: The growth of these tumors are quicker than grade II malignancies and spread to adjoining tissues. Grade IV: It is dangerous of all and likely to spread malignant tumors are in this category. Might even use blood vessels to speed up their growth. Xu et al. [4] developed an inertial microfluidic sorting device that has a 3D-stacked multistage intended for circulating tumor cells (CTCs) with efficient downstream analysis. The initial step includes a trapezoidal cross-section for maximum separation, followed by symmetrical square serpentine channels for more enrichment. This novel design produces rates yields over 80% efficiency and over 90% purity. This approach allows for quick and integrated CTC analysis.
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