Early　detection　of　liver　fibrosis　is　of　critical　importance.　The　homodyned　K　(HK)　distribution　is　a　generalized　model　with　the　most　physical　meaning　of　ultrasonic　backscattering.　The　stage　of　liver　fibrosis　can　be　evaluated　by　estimating　its　parameters　k　and　alpha.　However,　the　estimation　of　the　parameters　is　complicated.　Recently,　we　have　proposed　an　artificial　neural　network　(ANN)　estimator　for　the　HK　distribution.　In　this　work,　we　proposed　an　improved　ANN　estimator.　Monte　Carlo　computer　simulation　was　used　to　generate　independent　and　identically　distributed　envelope　signals　of　HK　distribution,　which　were　used　to　train　an　ANN　model.　Signal-to-noise　ratio　(SNR)　R,　skewness　S,　kurtosis　K,　X-,　and　U-statistics　were　calculated　from　envelope　signals　as　feature　parameters.　The　inputs　of　our　previous　ANN　estimator　were　R-2,　S-2,　K-2,　X　and　U.　In　this　work,　the　proposed　ANN　estimator　took　parameters　R-0.72,　S-0.72,　K-0.72,　R-0.88,　S-0.88,　K-0.88,　X　and　U　as　the　inputs.　Clinical　ultrasound　radiofrequency　(RF)　data　were　divided　into　Group　I　(n　=　94;　liver　fibrosis　with　no　hepatic　steatosis)　and　Group　II　(n　=　143;　liver　fibrosis　with　mild　to　severe　hepatic　steatosis).　The　RF　data　were　envelope-detected　and　the　eight　feature　parameters　were　calculated　as　the　input　to　the　trained　ANN　model　to　obtain　the　estimated　k　and　alpha　values.　The　receiver　operating　characteristic　(ROC)　curve　was　used　for　evaluating　the　diagnostic　values　of　k　and　alpha　based　on　the　ANN　estimator,　which　were　compared　with　those　based　on　the　XU　estimator.　Computer　simulation　results　show　that　the　proposed　ANN　estimator　outperforms　the　previous　ANN　estimator.　For　Group　I,　the　area　under　the　ROC　curve　obtained　using　the　k　parameter　based　on　the　proposed　ANN　and　conventional　XU　estimators　were　(0.80,　0.67,　0.60,　0.65)　and　(0.75,　0.62,　0.59,　0.67)　for　diagnosing　liver　＞=　F1,　＞=　F2,　＞=　F3,　＞=　F4,　respectively.　For　Group　II,　compared　with　the　XU　estimator,　the　ANN　estimator　had　comparable　diagnostic　performance　of　liver　fibrosis.　The　coexistence　of　hepatic　steatosis　affected　the　detection　and　diagnosis　of　liver　fibrosis　by　the　HK　distribution.