Computer Aided Diagnosis (CAD) systems are increasingly utilizing image analysis and Deep Learning (DL) techniques, due to their high accuracy in several medical imaging ﬁelds, including the detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) from peripheral blood samples. However, no method in the literature has speciﬁcally analyzed the focus quality of ALL images or proposed a technique for sharpening the samples in an adaptive way for the purpose of classiﬁcation. To address this issue, in this paper we propose the ﬁrst intelligent approach able to enhance blood sample images by an adaptive unsharpening method. The method uses image processing techniques and DL to normalize the radius of the cell, estimate the focus quality, adaptively improve the sharpness of the images, and then perform the classiﬁcation. We evaluated the methodology on a public database of ALL images, considering several state-of-the-art CNNs to perform the classiﬁcation, with results showing the validity of the proposed approach.
Acute Lymphoblastic Leukemia detection based on adaptive unsharpening and Deep Learning / A. Genovese, M.S. Hosseini, V. Piuri, K.N. Plataniotis, F. Scotti (PROCEEDINGS OF THE ... IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING). - In: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)[s.l] : IEEE, 2021. - ISBN 9781728176055. - pp. 1205-1209 (( convegno nternational Conference on Acoustics, Speech, and Signal Processing, ICASSP tenutosi a Toronto nel 2021 [10.1109/ICASSP39728.2021.9414362].
|Titolo:||Acute Lymphoblastic Leukemia detection based on adaptive unsharpening and Deep Learning|
|Parole Chiave:||Deep Learning; CNN; ALL; XAI|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
|Data di pubblicazione:||2021|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/ICASSP39728.2021.9414362|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|