1. A method (600) of analysing a blood smear image, the method comprising:
detecting (602), by a processor (104), a plurality of blood cells in the blood smear image
(300A) based on edge detection of the plurality of blood cells,
wherein the edge detection of the plurality of blood cells in the blood smear
image (300A) is based on a preprocessing of the blood smear image;
determining (604), by the processor (104), contours of each of the plurality of blood
cells based on the edge detection of the plurality of blood-cells;
determining (606), by the processor (104), a bounding box for each of the plurality of
blood-cells based on the contours of each of the plurality of blood-cells;
classifying (608), by the processor (104), each of the plurality of blood-cells as one of
a white blood cell (WBC) or a red blood cell (RBC) using a deep learning model,
wherein the deep learning model (212) is trained based on training data
comprising a plurality of images of WBCs and RBCs;
determining (610), by the processor (104), a count of WBCs, a count of RBCs and
volumetric information of the RBCs and the WBCs based on the classification and the contours
of each of the plurality of blood-cells; and
outputting (612), by the processor (104), a report comprising the count of WBCs, the
count of RBCs or the volumetric information of the RBCs and the WBCs.
2. The method (600) as claimed in claim 1, wherein the preprocessing comprises:
enhancing contrast, by the processor (104), of the blood smear image (300A) using a
histogram equalization technique; and
removing noise, by the processor (104), in the contrast enhanced blood smear image
(300B) using a gaussian filter.
3. The method (600) as claimed in claim 1, wherein the edges of each of the plurality of blood
cells are determined by determining a bimodal image (300C) of the blood smear image (300A)
upon the preprocessing, wherein the edges of each of the plurality of blood cells are determined
based on the bimodal image (300C) by using an edge detection technique.
4. The method (500) as claimed in claim 1, wherein the training data is generated by:
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inputting, by the processor (104), at least one training image to the deep learning model
(212),
wherein the at least one training image comprises at least one WBC and/or at
least one RBC;
determining, by the processor (104), the plurality of images of WBCs and RBCs by:
determining, by the processor (104), bounding boxes for each of the at least one
WBC and/or the at least one RBC based on detection of contours of the at least one
WBC and the at least one RBC in the at least one training image;
cropping, by the processor (104), each of the bounding boxes to determine at
least one WBC image and at least one RBC image;
generating, by the processor (104), a first set of samples corresponding to WBCs
and a second set of samples corresponding to RBCs based on the at least one WBC
image and the at least one RBC image respectively using a generator model of the deep
learning model (212),
wherein the first set of samples and the second set of samples are
generated based on a classification of each sample as one of real sample or fake
sample using a discriminator model of the deep learning model (212),
wherein samples corresponding to the first set of samples and the second
set of samples are generated until the first set of samples is balanced with respect
to the second set of samples and each of the first set of samples and the second
set of samples are classified as real by the discriminator model; and
wherein the plurality of images of WBCs and RBCs are determined
based on the first set of samples and the second set of samples, respectively.
5. The method (600) as claimed in claim 1, comprises:
inputting, by the processor (104), the report as a query to a generative artificial
intelligence-based query system (222); and
displaying, by the processor (104), an analysis received from the generative artificial
intelligence-based query system (222) on a display screen.
6. The method (600) as claimed in claim 5, wherein the analysis is based on the count of WBCs,
the count of RBCs or the volumetric information of the RBCs and WBCs,
wherein the analysis comprises one or more health conditions determined based on a
comparison of the count of WBCs, the count of RBCs or the volumetric information of the
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RBCs and the WBCs with a corresponding predefined threshold count of WBCs, a predefined
threshold count of RBCs or a predefined volumetric threshold of RBCs and WBCs.
7. The method (600) as claimed in claim 1, comprising:
classifying, by the processor (104), each of the plurality of blood-cells classified as the
WBC as one of a plurality of WBC classes using the deep learning model (212), wherein each
of the plurality of WBC classes correspond to a type of WBC from a plurality of WBC types.
8. A system (100) of analysing a blood smear image, comprising:
a processor (104); and
a memory (106) communicably coupled to the processor (104), wherein the memory
(106) stores processor-executable instructions, which, on executing by the processor (104),
cause the processor (104) to:
detect a plurality of blood cells in the blood smear image (300A) based on edge
detection of the plurality of blood cells,
wherein the edge detection of the plurality of blood cells in the blood smear
image (300A) is based on a preprocessing of the blood smear image (300A);
determine contours of each of the plurality of blood cells based on the edge detection
of the plurality of blood-cells;
determine a bounding box for each of the plurality of blood-cells based on the contours
of each of the plurality of blood-cells;
classify each of the plurality of blood-cells as one of a white blood cell (WBC) or a red
blood cell (RBC) using a deep learning model (212),
wherein the deep learning model (212) is trained based on training data
comprising a plurality of images of WBCs and RBCs;
determine a count of WBCs, a count of RBCs and volumetric information of the RBCs
and the WBCs based on the classification and the contours of each of the plurality of bloodcells; and
output a report comprising the count of WBCs, the count of RBCs or the volumetric
information of the RBCs and the WBCs.
9. The system (100) as claimed in claim 8, wherein to preprocess, the processor (104) is
configured to:
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enhance contrast of the blood smear image (300A) using a histogram equalization
technique; and
remove noise in the contrast enhanced blood smear image (300A) using a gaussian
filter.
10. The system (100) as claimed in claim 8, wherein the edges of each of the plurality of blood
cells are determined by determining a bimodal image (300C) of the blood smear image (300A)
upon the preprocessing, wherein the edges of each of the plurality of blood cells are determined
based on the bimodal image (300C) by using edge detection technique.