KHUSHBOO MUNIR

Dottoressa di ricerca

ciclo: XXXIV


relatore: prof. Antonello Rizzi, prof. Fabrizio Frezza

Titolo della tesi: Deep Learning Techniques for Clinical Diagnosis

Medical images play an important role in medical diagnosis and treatment. Oncologists analyze images to determine the different characteristics of the deadly diseases, to plan the therapy and to observe the evolution of the disease. The objective of this thesis is to propose efficient methods for detection of two deadliest diseases, i.e. COVID-19 and brain tumors. As concerns COVID-19, it will be detected using Chest X-ray scans, while brain tumors will be identified starting from Magnetic Resonance (MR) images performing suitable segmentation procedures. The latest technical literature concerning radiographic CT images of COVID-19 shows that deep learning methods can be implemented to extract specific features of COVID-19, aiding the clinical diagnosis. For this reason, most data scientists and AI researchers work on Machine Learning methods COVID-19 for designing automatic screening procedures. Indeed, an automated method would result in quicker segmentation findings and results that would not differ as much between hospitals with various resources, resulting in a more consistent identification of brain tumors and COVID19. To improve the performance of segmentation new architectures are proposed and tested in this thesis. We propose deep neural networks for detection of COVID-19, trained on the x-ray images of patients’ lungs. Proposed architecture are based on convolutional neural networks and inception modules for brain tumors segmentation. A comparison of these proposed architectures with the baseline reference ones shows very interesting results.

Produzione scientifica

11573/1657761 - 2022 - Deep learning hybrid techniques for brain tumor segmentation
Munir, Khushboo; Frezza, Fabrizio; Rizzi, Antonello - 01a Articolo in rivista
rivista: SENSORS (Basel : Molecular Diversity Preservation International (MDPI), 2001-) pp. 1-26 - issn: 1424-8220 - wos: WOS:000882152700001 (6) - scopus: 2-s2.0-85141600779 (8)

11573/1651716 - 2022 - Artificial intelligence for thyroid nodule characterization: where are we standing?
Sorrenti, Salvatore; Dolcetti, Vincenzo; Radzina, Maija; Bellini, Maria Irene; Frezza, Fabrizio; Munir, Khushboo; Grani, Giorgio; Durante, Cosimo; D'andrea, Vito; David, Emanuele; Calò, Pietro Giorgio; Lori, Eleonora; Cantisani, Vito - 01g Articolo di rassegna (Review)
rivista: CANCERS (Basel: MDPI) pp. 3357- - issn: 2072-6694 - wos: WOS:000832162400001 (35) - scopus: 2-s2.0-85136388586 (39)

11573/1584407 - 2021 - Detection and screening of COVID-19 through chest computed tomography radiographs using deep neural networks
Munir, Khushboo; Elahi, Hassan; Farooq, Muhammad Umar; Ahmed, Sana; Frezza, Fabrizio; Rizzi, Antonello - 02a Capitolo o Articolo
libro: Data Science for COVID-19 - (9780128245361)

11573/1438601 - 2021 - Brain tumor segmentation using 2D-UNET convolutional neural network
Munir, Khushboo; Frezza, Fabrizio; Rizzi, Antonello - 02a Capitolo o Articolo
libro: Deep learning for cancer diagnosis - (978-981-15-6320-1; 978-981-15-6321-8)

11573/1438603 - 2021 - Deep learning for brain tumor segmentation
Munir, Khushboo; Frezza, Fabrizio; Rizzi, Antonello - 02a Capitolo o Articolo
libro: Deep learning for cancer diagnosis - (978-981-15-6320-1; 978-981-15-6321-8)

11573/1448293 - 2020 - A review on applications of piezoelectric materials in aerospace industry
Elahi, H.; Munir, K.; Eugeni, M.; Abrar, M.; Khan, A.; Arshad, A.; Gaudenzi, P. - 01a Articolo in rivista
rivista: INTEGRATED FERROELECTRICS (Taylor & Francis Limited:Rankine Road, Basingstoke RG24 8PR United Kingdom:011 44 1256 813035, EMAIL: madeline.sims@tandf.co.uk, info@tandf.co.uk, INTERNET: http://www.tandf.co.uk, Fax: 011 44 1256 330245) pp. 25-44 - issn: 1058-4587 - wos: WOS:000576897400003 (33) - scopus: 2-s2.0-85092571260 (40)

11573/1306955 - 2019 - Cancer diagnosis using deep learning: A bibliographic review
Munir, Khushboo; Elahi, Hassan; Ayub, Afsheen; Frezza, Fabrizio; Rizzi, Antonello - 01g Articolo di rassegna (Review)
rivista: CANCERS (Basel: MDPI) pp. 1-36 - issn: 2072-6694 - wos: WOS:000489719000020 (186) - scopus: 2-s2.0-85071841965 (251)

11573/1439171 - 2017 - A low-power delta-sigma modulator ADC for sensor system applications
Munir, Khushboo; Hussain, Arshad - 04b Atto di convegno in volume
congresso: 3rd MULTI-DISCIPLINARY STUDENT RESEARCH CONFERENCE (Wah; Pakistan)
libro: MDSRC - 2017 Proceedings - ()

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