AIMC Topic: Deep Learning

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Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare.

Scientific reports
Brain tumor causes life-threatening consequences due to which its timely detection and accurate classification are critical for determining appropriate treatment plans while focusing on the improved patient outcomes. However, conventional approaches ...

Application of wings interferential patterns (WIPs) and deep learning (DL) to classify some Culex. spp (Culicidae) of medical or veterinary importance.

Scientific reports
In this paper, we test the possibility of using Wing Interference Patterns (WIPs) and deep learning (DL) for the identification of Culex mosquitoes species to evaluate the extent to which a generic method could be developed for surveying Dipteran ins...

Auto-Segmentation via deep-learning approaches for the assessment of flap volume after reconstructive surgery or radiotherapy in head and neck cancer.

Scientific reports
Reconstructive flap surgery aims to restore the substance and function losses associated with tumor resection. Automatic flap segmentation could allow quantification of flap volume and correlations with functional outcomes after surgery or post-opera...

An automatic classification of breast cancer using fuzzy scoring based ResNet CNN model.

Scientific reports
The expansion rate of medical data during the past ten years has rapidly expanded due to the vast fields. The automated disease diagnosis system is proposed using a deep learning (DL) algorithm, which automates and helps speed up the process efficien...

Deep learning based knowledge tracing in intelligent tutoring systems.

Scientific reports
The emergence of online education, e.g., intelligent tutoring system (ITS), complements or partially replaces conventional offline education, especially during the COVID-19 pandemic. Knowledge tracing (KT) plays a pivotal role in the intelligent tuto...

Deep learning-driven drug response prediction and mechanistic insights in cancer genomics.

Scientific reports
In the field of cancer therapy, the diversity and heterogeneity of cancer genomes in clinical patients complicate and challenge the effective use of non-targeted drugs, as these drugs often fail to address specific genetic events. Recent advancements...

Enhancing chronic wound assessment through agreement analysis and tissue segmentation.

Scientific reports
Accurate monitoring of chronic wound progression is crucial for assessing healing dynamics. However, the current manual process of tissue segmentation and quantification, which is an indicator of the healing progress, is time-consuming and subject to...

Uncovering subtype-specific metabolic signatures in breast cancer through multimodal integration, attention-based deep learning, and self-organizing maps.

Scientific reports
This study integrates multimodal metabolomic data from three platforms-LC-MS, GC-MS, and NMR-to systematically identify biomarkers distinguishing breast cancer subtypes. A feedforward attention-based deep learning model effectively selected 99 signif...

An enhanced deep learning model for accurate classification of ovarian cancer from histopathological images.

Scientific reports
Ovarian Cancer is a malignancy that develops from ovarian cells and is frequently characterized by aberrant cell proliferation that leads to the creation of tumors within the ovaries. The high death rate and often delayed discovery of Ovarian Cancer ...

Automatic melanoma detection using an optimized five-stream convolutional neural network.

Scientific reports
Melanoma is among the deadliest forms of malignant skin cancer, with the number of cases increasing dramatically worldwide. Its early and accurate diagnosis is crucial for effective treatment. However, automatic melanoma detection has several signifi...