Less Is More: A Leakage-Controlled Study of Dermoscopic Preprocessing for Joint Skin Lesion Classification and Segmentation with YOLO26

Truong Viet Vu; Nguyen Chi Hai; Nguyen Phuc Nguyen; Ngo Hoang Tu; Vo Nguyen Quoc Bao; Nguyen Thai Anh

cs.LGOct 6, 2026

Less Is More: A Leakage-Controlled Study of Dermoscopic Preprocessing for Joint Skin Lesion Classification and Segmentation with YOLO26

Authors: Truong Viet Vu, Nguyen Chi Hai, Nguyen Phuc Nguyen, Ngo Hoang Tu, Vo Nguyen Quoc Bao, Nguyen Thai Anh

Organizations: Faculty of Information Technology, Van Lang School of Technology, Van Lang University, Ho Chi Minh City, Vietnam

Abstract

Handcrafted preprocessing is widely employed in automated dermoscopic analysis to suppress imaging artifacts and enhance lesion visibility. Nevertheless, its actual contribution to modern real-time models remains unclear, particularly when evaluation protocols do not adequately control correlations among images of the same lesion. This study presents a leakage-controlled, lesion-disjoint evaluation of dermoscopic preprocessing and augmentation for joint multi-class lesion classification and instance segmentation using a fixed nano-scale YOLO26 segmentation model (YOLO26n-seg). From HAM10000 (10,015 images), quality control yields 10,013 valid image-mask pairs from 7,468 unique lesions, partitioned into mutually exclusive sets by lesion identity. With the architecture, resolution, training budget, and evaluation protocol held fixed, we compare minimally processed images plus online augmentation against offline class balancing, DullRazor-CLAHE preprocessing, and raw-processed hybrid views, over three random seeds. On the lesion-disjoint test set, the raw baseline achieves a mask mAP $_{50:95}$ of $0.5636 \pm 0.0234$ , a Dice score of $0.9356 \pm 0.0024$ , and a macro-F1 score of $0.6917 \pm 0.0202$ . Offline augmentation does not improve the mean performance, while the combined and hybrid strategies reduce both class-aware segmentation and classification accuracy. At only 2.69 million parameters, the model runs at approximately 50 frames per second. Under a leakage-controlled, lesion-disjoint protocol with all non-input factors held fixed, minimally processed dermoscopic images combined with standard online augmentation deliver a better accuracy-efficiency trade-off than increasingly complex deterministic preprocessing, which yields no consistent joint benefit across three seeds on HAM10000.

Figures & tables

Class	Train	Validation	Test	Total
akiec	229	49	49	327
bcc	360	77	77	514
bkl	768	165	165	1,098
df	80	18	17	115
mel	778	167	167	1,112
nv	4,693	1,006	1,006	6,705

Table I : Class distribution of the lesion-disjoint dataset.

Figure 1 : Overview of the experimental pipeline.

Configuration	Training views	Validation and test views	Offline augmentation
Raw + online augmentation	Raw image followed by label-safe letterboxing	Raw image followed by the same letterboxing	None
Offline class balancing	Baseline view plus three generated variants for every non- nv training image	Identical to the raw baseline	Train only
DullRazor–CLAHE	DullRazor hair removal followed by CIELAB CLAHE and letterboxing	The same deterministic preprocessing pipeline	None
Raw–processed hybrid	One raw view and one ROI–DullRazor–Gray World view for each training image	The minimally processed raw view	None

Table II : Principal preprocessing and augmentation configurations.

Input strategy	Acc.	BAcc.	Macro-F1	Dice	IoU	$\mathrm{BF}_{1}$	Mask $\mathrm{mAP}_{50}$	Mask $\mathrm{mAP}_{50:95}$
Raw + online augmentation	$\mathbf{0.8210{\pm}0.0111}$	$\mathbf{0.6672{\pm}0.0203}$	$\mathbf{0.6917{\pm}0.0202}$	$0.9356{\pm}0.0024$	$0.8971{\pm}0.0027$	$\mathbf{0.4922{\pm}0.0048}$	$\mathbf{0.7364{\pm}0.0232}$	$\mathbf{0.5636{\pm}0.0234}$
Raw without online augmentation	$0.7753{\pm}0.0070$	$0.5707{\pm}0.0373$	$0.5893{\pm}0.0279$	$0.9228{\pm}0.0006$	$0.8815{\pm}0.0008$	$0.4582{\pm}0.0008$	$0.5654{\pm}0.0366$	$0.4271{\pm}0.0252$
Offline class balancing	$0.8150{\pm}0.0047$	$0.6654{\pm}0.0154$	$0.6870{\pm}0.0251$	$0.9332{\pm}0.0021$	$0.8943{\pm}0.0020$	$0.4841{\pm}0.0051$	$0.7305{\pm}0.0168$	$0.5614{\pm}0.0131$
DullRazor–CLAHE	$0.8026{\pm}0.0010$	$0.6090{\pm}0.0147$	$0.6356{\pm}0.0129$	$0.9304{\pm}0.0036$	$0.8900{\pm}0.0038$	$0.4713{\pm}0.0021$	$0.6940{\pm}0.0133$	$0.5192{\pm}0.0122$
Raw–processed hybrid	$0.8186{\pm}0.0058$	$0.6252{\pm}0.0161$	$0.6537{\pm}0.0257$	$\mathbf{0.9366{\pm}0.0006}$	$\mathbf{0.8978{\pm}0.0006}$	$0.4846{\pm}0.0050$	$0.7119{\pm}0.0089$	$0.5466{\pm}0.0074$

Table III : Three-seed test performance of the main YOLO26n-seg configurations. Acc., BAcc., and BF 1 denote accuracy, balanced accuracy, and boundary F 1 score, respectively; bold values indicate the best mean.

Figure 2 : Main test-set comparison of the four principal input strategies and the deterministic no-augmentation control. Points denote three-seed means, horizontal error bars denote standard deviations, and dashed lines mark the raw baseline (raw + online augmentation).

Configuration	Acc.	BAcc.	Macro-F1	Dice	IoU	$\mathrm{BF}_{1}$	Mask $\mathrm{mAP}_{50}$	Mask $\mathrm{mAP}_{50:95}$
Raw (seed 42)	$\mathbf{0.8310}$	$0.6489$	$0.6796$	$0.9331$	$0.8943$	$0.4869$	$0.7340$	$0.5520$
DullRazor only	$0.8097$	$0.6020$	$0.6440$	$0.9329$	$0.8927$	$0.4821$	$0.6967$	$0.5224$
CLAHE only	$0.8150$	$0.6505$	$0.6623$	$0.9315$	$0.8927$	$0.4846$	$0.7366$	$0.5664$
Gray World only	$0.8064$	$0.6499$	$0.6730$	$\mathbf{0.9371}$	$\mathbf{0.8987}$	$\mathbf{0.4940}$	$0.7261$	$0.5605$
ROI only	$0.8263$	$\mathbf{0.6524}$	$\mathbf{0.6945}$	$0.9341$	$0.8962$	$0.4934$	$\mathbf{0.7405}$	$\mathbf{0.5729}$
ROI + DullRazor + CLAHE	$0.8097$	$0.6310$	$0.6580$	$0.9303$	$0.8902$	$0.4736$	$0.6942$	$0.5123$

Table IV : Component ablation on the lesion-disjoint test set using seed 42. The raw reference is the single-seed counterpart of the three-seed raw baseline in Table III . Bold values indicate the best result in each column.

Architecture	Params (M)	Size (MiB)	Macro-F1	Mask $\mathrm{mAP}_{50:95}$	Latency (ms)	FPS
YOLO11n-seg	2.84	5.76	$0.6734{\pm}0.0068$	$0.5501{\pm}0.0150$	$25.51{\pm}4.39$	$39.92{\pm}6.28$
YOLO12n-seg	2.78	5.74	$0.6962{\pm}0.0178$	$0.5758{\pm}0.0067$	$20.17{\pm}6.65$	$54.33{\pm}21.76$
YOLO26n-seg	2.69	6.28	$0.6917{\pm}0.0202$	$0.5636{\pm}0.0234$	$20.48{\pm}4.24$	$50.14{\pm}9.42$
YOLO26s-seg	10.37	22.29	$0.7239$	$0.5945$	$14.26$	$70.13$

Table V : Raw-input architecture and efficiency comparison. M and MiB denote millions of parameters and mebibytes; results are mean

\pm

standard deviation over three seeds, and YOLO26s-seg is reported for seed 42. FPS is the per-seed mean of ( 5 ), which differs slightly from

1000/\bar{\ell}_{\mathrm{ms}}

as the reciprocal and mean do not commute.

Figure 3 : Qualitative test predictions of the raw YOLO26n-seg baseline using seed 52. The upper examples are correctly classified, whereas the lower examples retain high mask overlap but receive incorrect diagnostic labels.

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Skin Lesion Classification Lesion Segmentation Melanoma Deep Learning Model Fine-Tuning

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Automated segmentation of skin lesions using deep learning models for dermoscopic images can be very helpful in finding melanomas earlier than they would normally be detected. However, most deep learning methods available do not perform well. The aim of this paper is to present a parameter-efficient fine-tuning method called PEFT-MedSAM for adapting the Medical Segment Anything Model (MedSAM) to automatically segment dermoscopic skin lesions. The PEFT-MedSAM method uses only the lightweight mask decoder for training the model while keeping the pre-trained image encoder and prompt encoder frozen. The experiments performed on the ISIC 2018 benchmark dataset shows that PEFT-MedSAM obtains a dice coefficient of .9411 and an intersection over union value of .8918 when compared to both a fully trained U-Net baseline (.8715 dice coefficient) and zero-shot MedSAM inference (.8997 dice coefficient). The external validation of the model using PH2 dataset shows .9467 dice coefficient with +/- .0310 standard deviation. Supportive evidence for these claims include a p-value less than .0001 for Wilcoxon signed rank tests comparing the two datasets and bootstrap-estimated 95% confidence intervals of [.9364,.9447] that represent the estimated range of possible values for the average dice coefficient obtained by repeating the test. To increase clinical trustworthiness, we used Grad-CAM explainability along with a pointing game based evaluation methodology to evaluate the CNN baseline model on the validation set. The results showed that we had an accuracy rate of 98.27% on the validation set of 519 images and confirmed that the model classified regions containing skin lesions.

Abstract

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