Person: Gohel, Bakul
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Bakul Gohel
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Publication Metadata only Digital mammography dataset for breast cancer diagnosis research (DMID) with breast mass segmentation analysis(Springer, 01-03-2024) Oza, Parita; Oza, Urvi; Sharma, Paawan; Patel, Samir; Kumar, Pankaj; Gohel, Bakul; DA-IICT, Gandhinagar; Oza, Urvi (201921009)Purpose:In the last two decades, computer-aided detection and diagnosis (CAD) systems have been created to help radiologists discover and diagnose lesions observed on breast imaging tests. These systems can serve as a second opinion tool for the radiologist. However, developing algorithms for identifying and diagnosing breast lesions relies heavily on mammographic datasets. Many existing databases do not consider all the needs necessary for research and study, such as mammographic masks, radiology reports, breast composition, etc. This paper aims to introduce and describe a new mammographic database.�Methods:The proposed dataset comprises mammograms with several lesions, such as masses, calcifications, architectural distortions, and asymmetries. In addition, a radiologist report is provided, describing the details of the breast, such as breast density, description of abnormality present, condition of the skin, nipple and pectoral muscles, etc., for each mammogram.�Results:We present results of commonly used segmentation framework trained on our proposed dataset. We used information regarding the class of abnormalities (benign or malignant) and breast tissue density provided with each mammogram to analyze the segmentation model�s performance concerning these parameters.�Conclusion:The presented dataset provides diverse mammogram images to develop and train models for breast cancer diagnosis applications.Publication Metadata only Deep learning-based Automated Localisation of Anterior Commissure and Posterior Commissure Landmarks in 3D space from three-plane 2D MRI localiser slices of the brain(Elsevier, 31-01-2023) Gohel, Bakul; Kumar, Lalit; Shah, Divya; DA-IICT, Gandhinagar; Kumar, Lalit (202012008); Shah, Divya (202111040)The operator first performs the three-plane MRI localiser slices acquisition protocol during brain MRI scan acquisition. Based on various anatomical landmarks such as anterior commissure AC), posterior commissure (PC), and mid-sagittal plane (MSP), the operator plans MRI position and orientation before a full brain MRI scan which is essential for good quality MRI. Automatic localisation of these landmarks is vital to automatise the process and minimise operator error. Prior approaches focused on automated AC and PC detection on 2D mid-sagittal MRI slices. However, improper head positioning leads to improper 2D mid-sagittal MRI slice; therefore, it may impact the localisation error, and the localisation is not in 3D space with respect to brain volume. In the present work, the AC and PC landmarks' locations were predicted in 3D space from three-plane 2D MRI localiser slices using a convolutional neural network-based approach. Six publically available brain MRI datasets were used. The mean AC and PC localisation error obtained was less than 2mm in a within-dataset evaluation and less than 3mm in a cross-dataset evaluation.Publication Metadata only EEG/MEG source imaging in the absence of subject's brain MRI scan: Perspective on co-registration and MRI selection approach(Wiley, 01-01-2023) Gohel, Bakul; Khare, Manish; DA-IICT, GandhinagarEEG/MEG source localization requires a subject's brain MRI to compute the sourcemodel and headmodel.�As part of this computation, co-registration of the digitized head information and brain MRI scan is the essential step. However, in the absence of a brain MRI scan, an approximated sourcemodel and headmodel can be computed from the subject's digitized head information and brain MRI scans from other subjects. In the present work, we compared the fiducial (FID)- and iterative closet point (ICP)-based co-registration approaches for computing an approximated sourcemodel using single and multiple available brain MRI scans. We also evaluated the two different template MRI selection strategies: one is based on objective registration error, and another on sourcemodel approximation error. The outcome suggests that averaged approximated solutions using multiple template brain MRI scans showed better performance than single-template MRI-based solutions. The FID-based approach performed better than the ICP-based approach for co-registration of the digitized head surface and brain MRI scan. While selecting template MRIs, the selection approach based on objective registration error showed better performance than a sourcemodel approximation error-based criterion. Cross-dataset performance analysis showed a higher model approximation error than within-dataset analysis. In conclusion, the FID-based co-registration approach and objective registration error-based MRI selection criteria provide a simple, fast and more accurate solution to compute averaged approximated models compared with the ICP-based approach. The demography of brain MRI scans should be similar to that of the query subject whose brain MRI scan was unavailable.Publication Metadata only Neural Network-based Fast and Intelligent Signal Integrity Assessment Model for Emerging MWCNT Bundle On-Chip Interconnects in Integrated Circuit(Taylor & Francis, 26-02-2023) Bhatti, Gulafsha; Pathade, Takshashila; Agrawal, Yash; Palaparthy, Vinay; Gohel, Bakul; Parekh, Rutu; Kumar, Mekala Girish; DA-IICT, Gandhinagar; Gulafsha Bhatti (202021005); Takshashila Pathade (201621013)At nanometer technology nodes, the efficient signal integrity and performance assessment of vast on-chip interconnects are crucial and challenging. For a long time, copper (Cu) has been used as an interconnect material in integrated circuits (ICs). However, as heading towards lower technology nodes, Cu is becoming inadequate to satisfy the requirements for high-speed applications due to its physical limitations. To mitigate this issue, a multiwall carbon nanotube bundle (MWCNTB) is proven to be a better replacement for Cu. Hence, the current work innovatively focuses on modeling, analysis, and performance evaluation of MWCNTB interconnects at 32?nm technology nodes using various machine learning (ML) and neural network (NN) based techniques for signal integrity assessment and fast computation of on-chip interconnect design. Based on the results obtained by comparing the different performance parameters, it is envisaged that NN-based ADAM technique leads to the best-suited model. The developed model is fruitful in evaluating the output performance of the system, such as power-delay-product (PDP), performing parametric analysis, and predicting optimum input design parameters of the driver-interconnect-load (DIL) system. This work utilizes HSPICE and Python electronic design automation tools for its implementation.