The timely diagnosis of acute lymphoblastic leukemia (ALL) is of paramount importance for enhancing the treatment efficacy and the survival rates of patients. In this study, we seek to introduce an ensemble-ALL model for the image classification of ALL, with the goal of enhancing early diagnostic capabilities and streamlining the diagnostic and treatment processes for medical practitioners. In this study, a publicly available dataset is partitioned into training, validation, and test sets. A diverse set of convolutional neural networks, including InceptionV3, EfficientNetB4, ResNet50, CONV_POOL-CNN, ALL-CNN, Network in Network, and AlexNet, are employed for training. The top-performing four individual models are meticulously chosen and integrated with the squeeze-and-excitation (SE) module. Furthermore, the two most effective SE-embedded models are harmoniously combined to create the proposed ensemble-ALL model. This model leverages the Bayesian optimization algorithm to enhance its performance. The proposed ensemble-ALL model attains remarkable accuracy, precision, recall, F1-score, and kappa scores, registering at 96.26, 96.26, 96.26, 96.25, and 91.36%, respectively. These results surpass the benchmarks set by state-of-the-art studies in the realm of ALL image classification. This model represents a valuable contribution to the field of medical image recognition, particularly in the diagnosis of acute lymphoblastic leukemia, and it offers the potential to enhance the efficiency and accuracy of medical professionals in the diagnostic and treatment processes.
Citation: Mei-Ling Huang, Zong-Bin Huang. An ensemble-acute lymphoblastic leukemia model for acute lymphoblastic leukemia image classification[J]. Mathematical Biosciences and Engineering, 2024, 21(2): 1959-1978. doi: 10.3934/mbe.2024087
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The timely diagnosis of acute lymphoblastic leukemia (ALL) is of paramount importance for enhancing the treatment efficacy and the survival rates of patients. In this study, we seek to introduce an ensemble-ALL model for the image classification of ALL, with the goal of enhancing early diagnostic capabilities and streamlining the diagnostic and treatment processes for medical practitioners. In this study, a publicly available dataset is partitioned into training, validation, and test sets. A diverse set of convolutional neural networks, including InceptionV3, EfficientNetB4, ResNet50, CONV_POOL-CNN, ALL-CNN, Network in Network, and AlexNet, are employed for training. The top-performing four individual models are meticulously chosen and integrated with the squeeze-and-excitation (SE) module. Furthermore, the two most effective SE-embedded models are harmoniously combined to create the proposed ensemble-ALL model. This model leverages the Bayesian optimization algorithm to enhance its performance. The proposed ensemble-ALL model attains remarkable accuracy, precision, recall, F1-score, and kappa scores, registering at 96.26, 96.26, 96.26, 96.25, and 91.36%, respectively. These results surpass the benchmarks set by state-of-the-art studies in the realm of ALL image classification. This model represents a valuable contribution to the field of medical image recognition, particularly in the diagnosis of acute lymphoblastic leukemia, and it offers the potential to enhance the efficiency and accuracy of medical professionals in the diagnostic and treatment processes.
We consider numerical invariants associated with polynomial identities of algebras over a field of characteristic zero. Given an algebra
limn→∞n√cn(A) | (1.1) |
exist and what are its possible values? In case of existence, the limit (1.1) is called the PI-exponent of
Nevertheless, the answer to Amitsur's question in the general case is negative: a counterexample was presented in [14]. Namely, for any real
The main goal of the present paper is to construct a series of unital algebras such that
Let
cn(A)=dimPnPn∩Id(A). |
If the sequence
exp_(A)=lim infn→∞n√cn(A),¯exp(A)=lim supn→∞n√cn(A), |
are well-defined. An existence of ordinary PI-exponent (1.1) is equivalent to the equality
In [14], an algebra
Clearly, polynomial identities of
f=∑fi1,…,ik,{i1,…,ik}⊆{1,…,n},0≤k≤n, | (2.1) |
where
Remark 2.1. A multilinear polynomial
The next statement easily follows from Remark 2.1.
Remark 2.2. Suppose that an algebra
Using results of [13], we obtain the following inequalities.
Lemma 2.1. ([13,Theorem 2]) Let
Lemma 2.2. ([13,Theorem 3]) Let
Given an integer
{a,b,zi1,…,ziT|i=1,2,…} |
and by the multiplication table
zija={zij+1ifj≤T−1,0ifj=T |
for all
ziTb=zi+11,i≥1. |
All other products of basis elements are equal to zero. Clearly, algebra
x1(x2x3)≡0 | (2.2) |
is an identity of
We will use the following properties of algebra
Lemma 2.3. ([14,Lema 2.1]) Let
Lemma 2.4. ([14,Lema 2.2]) Let
cn(BT)≥k!=(N−1T)!. |
Lemma 2.5. ([14,Lema 2.3]) Any multilinear identity
Let
QN=F[θ]0(QN+1), |
where
R=B(T1,N1)⊕B(T2,N2)⊕⋯, | (2.3) |
where
Let
Lemma 2.6. For any
(a) if
Pn∩Id(R)=Pn∩Id(B(Ti,Ni)⊕B(Ti+1,Ni+1))=Pn∩Id(BTi⊕BTi+1); |
(b) if
Pn∩Id(R)=Pn∩Id(B(Ti+1,Ni+1))=Pn∩(Id(BTi+1)). |
Proof. This follows immediately from the equality
The folowing remark is obvious.
Remark 2.3. Ler
Id(R♯)=Id(B(T1,N1)♯⊕B(T2,N2)♯⊕⋯). |
Theorem 3.1. For any real
Proof. Note that
cn(A)≤ncn−1(A) | (3.1) |
for any algebra
2m3<αm | (3.2) |
for all
cn(BT1)<αnfor alln≤N1−1andcN1(BT1)≥αN1. |
Consider an arbitrary
cn(R♯)≤n∑k=0(nk)ck(R)=Σ′1+Σ′2, |
where
Σ′1=N1∑k=0(nk)ck(R),Σ′2=n∑k=N1+1(nk)ck(R). |
By Lemma 2.6, we have
Σ1=N1∑k=0(nk)ck(BT1),Σ2=n∑k=0(nk)ck(BT2). |
Then for any
Σ2≤n∑k=0(nk)2k3≤2n3n∑k=0(nk)=2n32n, | (3.3) |
which follows from (3.2), provided that
Let us find an upper bound for
Σ1≤N1αN1N1∑k=0(nk) | (3.4) |
which follows from the choice of
From the Stirling formula
m!=√2πm(me)me112m+θm,0<θm<1, |
it follows that
(nk)≤√nk(n−k)⋅nnkk(n−k)n−k. | (3.5) |
Now we define the function
Φ(x)=1xx(1−x)1−x. |
It is not difficult to show that
(nk)≤√Φ(kn)⋅Φ(kn)n<2Φ(kn)n≤2Φ(N1n)n | (3.6) |
provided that
Σ1≤2N1αN1(N1+1)Φ(N1n)n,Σ2≤2n32n. |
Since
limn→∞Φ(N1n)n=1 |
and
2N1(N1+1)αN1Φ(N1n)n+2n32n<(2+12)n. | (3.7) |
Now we take
cn(R♯)<(2+12)n |
for
As soon as
cn(R)<αn+2n3 | (3.8) |
for all
αn≤cn(R)<αn+n(αn−1+2n3) | (3.9) |
for all
2Nj(Nj+1)αNjΦ(NjTj+1)Tj+1+2T3j+1⋅2Tj+1<(2+12j)Tj+1 | (3.10) |
for all
Let us denote by
cn(R♯α)<(2+12j)n | (3.11) |
if
exp_(R♯α)≤2. | (3.12) |
On the other hand, since
exp_(R♯α)≥1. | (3.13) |
Since the PI-exponent of non-nilpotent algebra cannot be strictly less than
exp_(Rα)=1,exp_(R♯α)=2. |
Finally, relations (3.8), (3.9) imply the equality
As a consequence of Theorem 3.1 we get an infinite family of unital algebras of exponential codimension growth without ordinary PI-exponent.
Corollary 1. Let
We would like to thank the referee for comments and suggestions.
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